Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method of encoding three-dimensional points includes: calculating a predicted value of attribute information of a first three-dimensional point in a prediction mode, using one or more items of attribute information of one or more second three-dimensional points in the vicinity of the first three-dimensional point; calculating a prediction residual that is a difference between the attribute information of the first three-dimensional point and the predicted value; and generating a bitstream including the prediction residual and prediction mode information indicating the prediction mode. The prediction mode is: one prediction mode among two or more prediction modes when a type of the attribute information of the first three-dimensional point is first attribute information including elements more than a predetermined threshold value; and one fixed prediction mode when the type is second attribute information including elements equal to or less than the predetermined threshold value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. continuation application of PCT InternationalPatent Application Number PCT/JP2021/036760 filed on Oct. 5, 2021,claiming the benefit of priority of U.S. Provisional Patent ApplicationNo. 63/088,001 filed on Oct. 6, 2020, the entire contents of which arehereby incorporated by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point cloud in a three-dimensional space. In the pointcloud scheme, the positions and colors of a point cloud are stored.While point cloud is expected to be a mainstream method of representingthree-dimensional data, a massive amount of data of a point cloudnecessitates compression of the amount of three-dimensional data byencoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include Moving Picture ExpertsGroup-4 Advanced Video Coding (MPEG-4 AVC) and High Efficiency VideoCoding (HEVC) standardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle by using three-dimensionalmap data is known (for example, see International Publication WO2014/020663).

SUMMARY

There has been a demand for increasing the encoding efficiency inthree-dimensional data encoding.

An object of the present disclosure is to provide a three-dimensionaldata encoding method, a three-dimensional data decoding method, athree-dimensional data encoding device, or a three-dimensional datadecoding device that is capable of increasing the encoding efficiency.

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method of encoding three-dimensionalpoints includes: calculating a predicted value of attribute informationof a first three-dimensional point in a prediction mode for calculatingthe predicted value, using one or more items of attribute information ofone or more second three-dimensional points in a vicinity of the firstthree-dimensional point, the first three-dimensional point and the oneor more second three-dimensional points being included in thethree-dimensional points; calculating a prediction residual that is adifference between the attribute information of the firstthree-dimensional point and the predicted value calculated; andgenerating a bitstream including the prediction residual and predictionmode information indicating the prediction mode, wherein the predictionmode is: one prediction mode among two or more prediction modes when atype of the attribute information of the first three-dimensional pointis first attribute information including elements a total number ofwhich is greater than a predetermined threshold value; and one fixedprediction mode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method of decoding three-dimensionalpoints includes: obtaining a prediction residual and prediction modeinformation indicating a prediction mode for a first three-dimensionalpoint among the three-dimensional points, by obtaining a bitstream;calculating a predicted value in the prediction mode indicated by theprediction mode information; and calculating attribute information ofthe first three-dimensional point by adding the predicted value to theprediction residual, wherein the prediction mode is: one prediction modeamong two or more prediction modes when a type of the attributeinformation of the first three-dimensional point is first attributeinformation including elements a total number of which is greater than apredetermined threshold value; and one fixed prediction mode when thetype of the attribute information of the first three-dimensional pointis second attribute information including elements a total number ofwhich is smaller than or equal to the predetermined threshold value.

The present disclosure provides a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device thatis capable of increasing the encoding efficiency.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram illustrating a configuration of a three-dimensionaldata encoding and decoding system according to Embodiment 1;

FIG. 2 is a diagram illustrating a structure example of point cloud dataaccording to Embodiment 1;

FIG. 3 is a diagram illustrating a structure example of a data fileindicating the point cloud data according to Embodiment 1;

FIG. 4 is a diagram illustrating types of the point cloud data accordingto Embodiment 1;

FIG. 5 is a diagram illustrating a structure of a first encoderaccording to Embodiment 1;

FIG. 6 is a block diagram illustrating the first encoder according toEmbodiment 1;

FIG. 7 is a diagram illustrating a structure of a first decoderaccording to Embodiment 1;

FIG. 8 is a block diagram illustrating the first decoder according toEmbodiment 1;

FIG. 9 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 1;

FIG. 10 is a diagram showing an example of geometry informationaccording to Embodiment 1;

FIG. 11 is a diagram showing an example of an octree representation ofgeometry information according to Embodiment 1;

FIG. 12 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 1;

FIG. 13 is a block diagram of an attribute information encoder accordingto Embodiment 1;

FIG. 14 is a block diagram of an attribute information decoder accordingto Embodiment 1;

FIG. 15 is a block diagram showing a configuration of the attributeinformation encoder according to the variation of Embodiment 1;

FIG. 16 is a block diagram of the attribute information encoderaccording to Embodiment 1;

FIG. 17 is a block diagram showing a configuration of the attributeinformation decoder according to the variation of Embodiment 1;

FIG. 18 is a block diagram of the attribute information decoderaccording to Embodiment 1;

FIG. 19 is a diagram illustrating a structure of a second encoderaccording to Embodiment 1;

FIG. 20 is a block diagram illustrating the second encoder according toEmbodiment 1;

FIG. 21 is a diagram illustrating a structure of a second decoderaccording to Embodiment 1;

FIG. 22 is a block diagram illustrating the second decoder according toEmbodiment 1;

FIG. 23 is a diagram illustrating a protocol stack related to PCCencoded data according to Embodiment 1;

FIG. 24 is a diagram illustrating structures of an encoder and amultiplexer according to Embodiment 2;

FIG. 25 is a diagram illustrating a structure example of encoded dataaccording to Embodiment 2;

FIG. 26 is a diagram illustrating a structure example of encoded dataand a NAL unit according to Embodiment 2;

FIG. 27 is a diagram illustrating a semantics example ofpcc_nal_unit_type according to Embodiment 2;

FIG. 28 is a diagram illustrating an example of a transmitting order ofNAL units according to Embodiment 2;

FIG. 29 is a flowchart of processing performed by a three-dimensionaldata encoding device according to Embodiment 2;

FIG. 30 is a flowchart of processing performed by a three-dimensionaldata decoding device according to Embodiment 2;

FIG. 31 is a flowchart of multiplexing processing according toEmbodiment 2;

FIG. 32 is a flowchart of demultiplexing processing according toEmbodiment 2;

FIG. 33 is a diagram illustrating an example of three-dimensional pointsaccording to Embodiment 3;

FIG. 34 is a diagram illustrating an example of setting LoDs accordingto Embodiment 3;

FIG. 35 is a diagram illustrating an example of setting LoDs accordingto Embodiment 3;

FIG. 36 is a diagram illustrating an example of attribute information tobe used for predicted values according to Embodiment 3;

FIG. 37 is a diagram illustrating examples of exponential-Golomb codesaccording to Embodiment 3;

FIG. 38 is a diagram indicating a process on exponential-Golomb codesaccording to Embodiment 3;

FIG. 39 is a diagram indicating an example of a syntax in attributeheader according to Embodiment 3;

FIG. 40 is a diagram indicating an example of a syntax in attribute dataaccording to Embodiment 3;

FIG. 41 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 3;

FIG. 42 is a flowchart of an attribute information encoding processaccording to Embodiment 3;

FIG. 43 is a diagram indicating processing on exponential-Golomb codesaccording to Embodiment 3;

FIG. 44 is a diagram indicating an example of a reverse lookup tableindicating relationships between remaining codes and the values thereofaccording to Embodiment 3;

FIG. 45 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 3;

FIG. 46 is a flowchart of an attribute information decoding processaccording to Embodiment 3;

FIG. 47 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 3;

FIG. 48 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 3;

FIG. 49 is a diagram showing a first example of a table representingpredicted values calculated in prediction modes according to Embodiment4;

FIG. 50 is a diagram showing examples of attribute information items(pieces of attribute information) used as the predicted values accordingto Embodiment 4;

FIG. 51 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 52 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 53 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 54 is a diagram showing a fifth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 55 is a diagram showing a sixth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 56 is a diagram showing a seventh example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 57 is a diagram showing an eighth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 58 is a diagram showing a first example of a binarization table inbinarizing and encoding prediction mode values according to Embodiment4;

FIG. 59 is a diagram showing a second example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 4;

FIG. 60 is a diagram showing a third example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 4;

FIG. 61 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 4;

FIG. 62 is a flowchart of an example of encoding a prediction mode valueaccording to Embodiment 4;

FIG. 63 is a flowchart of an example of decoding a prediction mode valueaccording to Embodiment 4;

FIG. 64 is a diagram showing another example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4;

FIG. 65 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 4;

FIG. 66 is flowchart of another example of encoding a prediction modevalue according to Embodiment 4;

FIG. 67 is a flowchart of another example of decoding a prediction modevalue according to Embodiment 4;

FIG. 68 is a flowchart of an example of a process of determining whetheror not a prediction mode value is fixed under condition A in encodingaccording to Embodiment 4;

FIG. 69 is a flowchart of an example of a process of determining whetherthe prediction mode value is fixed or decoded under condition A indecoding according to Embodiment 4;

FIG. 70 is a diagram for describing maximum absolute differential valuemaxdiff according to Embodiment 4;

FIG. 71 is a diagram showing an example of a syntax according toEmbodiment 4;

FIG. 72 is a diagram showing an example of a syntax according toEmbodiment 4;

FIG. 73 is a flowchart of a three-dimensional data encoding process by athree-dimensional data encoding device according to Embodiment 4;

FIG. 74 is a flowchart of an attribute information encoding processaccording to Embodiment 4;

FIG. 75 is a flowchart of a predicted value calculation process by thethree-dimensional data encoding device according to Embodiment 4;

FIG. 76 is a flowchart of a prediction mode selection process accordingto Embodiment 4;

FIG. 77 is a flowchart of a prediction mode selection process thatminimizes cost according to Embodiment 4;

FIG. 78 is a flowchart of a three-dimensional data decoding process by athree-dimensional data decoding device according to Embodiment 4;

FIG. 79 is a flowchart of an attribute information decoding processaccording to Embodiment 4;

FIG. 80 is a flowchart of a predicted value calculation process by thethree-dimensional data decoding device according to Embodiment 4;

FIG. 81 is a flowchart of a prediction mode decoding process accordingto Embodiment 4;

FIG. 82 is a block diagram showing a configuration of an attributeinformation encoder included in the three-dimensional data encodingdevice according to Embodiment 4;

FIG. 83 is a block diagram showing a configuration of an attributeinformation decoder included in the three-dimensional data decodingdevice according to Embodiment 4;

FIG. 84 is a flowchart of a prediction mode determination processperformed by a three-dimensional data encoding device according toEmbodiment 5;

FIG. 85 is a diagram showing an example of a syntax of the predictionmode determination process performed by the three-dimensional dataencoding device according to Embodiment 5;

FIG. 86 is a flowchart of a prediction mode determination processperformed by a three-dimensional data decoding device according toEmbodiment 5;

FIG. 87 is a diagram showing an example of a syntax of attribute datawhen a prediction mode fixing flag is provided for eachthree-dimensional point according to Embodiment 5;

FIG. 88 is a diagram showing an example of a syntax of attribute datawhen a prediction mode fixing flag is provided for each LoD according toEmbodiment 5;

FIG. 89 is a flowchart of an example of a prediction mode encodingprocess by the three-dimensional data encoding device according toEmbodiment 5;

FIG. 90 is a flowchart of an example of a prediction mode decodingprocess by the three-dimensional data decoding device according toEmbodiment 5;

FIG. 91 is a flowchart of a three-dimensional data encoding process bythe three-dimensional data encoding device according to Embodiment 5;

FIG. 92 is a flowchart of an attribute information encoding processshown in FIG. 91 ;

FIG. 93 is a flowchart of a predicted-value calculation process shown inFIG. 92 ;

FIG. 94 is a flowchart of a prediction mode selection process shown inFIG. 93 ;

FIG. 95 is a flowchart of a specific example of the prediction modeselection process shown in FIG. 94 ;

FIG. 96 is a flowchart of a three-dimensional data decoding process bythe three-dimensional data decoding device according to Embodiment 5;

FIG. 97 is a flowchart of a predicted-value calculation process shown inFIG. 96 ;

FIG. 98 is a flowchart of details of the predicted-value calculationprocess shown in FIG. 97 ;

FIG. 99 is a flowchart of a calculation process of a prediction mode anda quantized value shown in FIG. 98 ;

FIG. 100 is a flowchart of a process when the three-dimensional dataencoding device does not fix a prediction mode according to Embodiment5;

FIG. 101 is a flowchart of a process when the three-dimensional datadecoding device does not fix a prediction mode according to Embodiment5;

FIG. 102 is a diagram showing another example of a syntax of attributedata according to Embodiment 5;

FIG. 103 is a flowchart of an example of a prediction mode encodingprocess by the three-dimensional data encoding device according toEmbodiment 5;

FIG. 104 is a flowchart of a prediction mode decoding process by thethree-dimensional data decoding device according to Embodiment 5;

FIG. 105 is a flowchart of another example of the predicted-valuecalculation process shown in FIG. 92 ;

FIG. 106 is a flowchart of a prediction mode selection process shown inFIG. 105 ;

FIG. 107 is a flowchart of a specific example of the prediction modeselection process shown in FIG. 106 ;

FIG. 108 is a flowchart of another example of the calculation process ofa prediction mode and a quantized value shown in FIG. 97 ;

FIG. 109 is a diagram illustrating an example of a firstthree-dimensional point and one or more second three-dimensional pointsin the vicinity of the first three-dimensional point according toEmbodiment 6.

FIG. 110 is a diagram illustrating an example of the firstthree-dimensional point and the one or more second three-dimensionalpoints in the vicinity of the first three-dimensional point according toEmbodiment 6.

FIG. 111 is a diagram illustrating an example of the firstthree-dimensional point and the one or more second three-dimensionalpoints in the vicinity of the first three-dimensional point according toEmbodiment 6.

FIG. 112 is a graph illustrating a relation between a cost and a bitcount according to Embodiment 6.

FIG. 113 is a graph illustrating results of changing an offset accordingto Embodiment 6.

FIG. 114 is a graph illustrating results of changing λ according toEmbodiment 6.

FIG. 115 is a diagram illustrating a first example of a binarizationtable in the case where a prediction mode value is binarized and encodedaccording to Embodiment 6.

FIG. 116 is a diagram illustrating a second example of the binarizationtable in the case where the prediction mode value is binarized andencoded according to Embodiment 6.

FIG. 117 is a diagram illustrating a third example of the binarizationtable in the case where the prediction mode value is binarized andencoded according to Embodiment 6.

FIG. 118 is a flowchart illustrating a method of determining whether ornot to fix a prediction mode by a three-dimensional data encoding deviceaccording to Embodiment 6.

FIG. 119 is a flowchart illustrating a decoding method by athree-dimensional data decoding device according to Embodiment 6.

FIG. 120 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 6.

FIG. 121 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 6.

FIG. 122 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 7;

FIG. 123 is a flowchart of a three-dimensional data creation methodaccording to Embodiment 7;

FIG. 124 is a diagram showing a structure of a system according toEmbodiment 7;

FIG. 125 is a block diagram of a client device according to Embodiment7;

FIG. 126 is a block diagram of a server according to Embodiment 7;

FIG. 127 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 7;

FIG. 128 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 7;

FIG. 129 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 7;

FIG. 130 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 7;

FIG. 131 is a diagram showing a structure of a variation of the systemaccording to Embodiment 7;

FIG. 132 is a diagram showing a structure of the server and clientdevices according to Embodiment 7;

FIG. 133 is a diagram illustrating a configuration of a server and aclient device according to Embodiment 7;

FIG. 134 is a flowchart of a process performed by the client deviceaccording to Embodiment 7;

FIG. 135 is a diagram illustrating a configuration of a sensorinformation collection system according to Embodiment 7;

FIG. 136 is a diagram illustrating an example of a system according toEmbodiment 7;

FIG. 137 is a diagram illustrating a variation of the system accordingto Embodiment 7;

FIG. 138 is a flowchart illustrating an example of an applicationprocess according to Embodiment 7;

FIG. 139 is a diagram illustrating the sensor range of various sensorsaccording to Embodiment 7;

FIG. 140 is a diagram illustrating a configuration example of anautomated driving system according to Embodiment 7;

FIG. 141 is a diagram illustrating a configuration example of abitstream according to Embodiment 7;

FIG. 142 is a flowchart of a point cloud selection process according toEmbodiment 7;

FIG. 143 is a diagram illustrating a screen example for point cloudselection process according to Embodiment 7;

FIG. 144 is a diagram illustrating a screen example of the point cloudselection process according to Embodiment 7; and

FIG. 145 is a diagram illustrating a screen example of the point cloudselection process according to Embodiment 7.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In accordance with an aspect of the present disclosure, athree-dimensional data encoding method of encoding three-dimensionalpoints includes: calculating a predicted value of attribute informationof a first three-dimensional point in a prediction mode for calculatingthe predicted value, using one or more items of attribute information ofone or more second three-dimensional points in a vicinity of the firstthree-dimensional point, the first three-dimensional point and the oneor more second three-dimensional points being included in thethree-dimensional points; calculating a prediction residual that is adifference between the attribute information of the firstthree-dimensional point and the predicted value calculated; andgenerating a bitstream including the prediction residual and predictionmode information indicating the prediction mode, wherein the predictionmode is: one prediction mode among two or more prediction modes when atype of the attribute information of the first three-dimensional pointis first attribute information including elements a total number ofwhich is greater than a predetermined threshold value; and one fixedprediction mode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.

By the above-described three-dimensional data encoding method, attributeinformation including many elements can be encoded by one predictionmode selected from two or more prediction modes, thereby reducingoverhead and increasing the encoding efficiency of the attributeinformation.

For example, it is possible that the bitstream indicates whether or notthe prediction mode is the one fixed prediction mode.

For example, it is possible that the predetermined threshold value is 1.

For example, it is possible that the bitstream does not include theprediction mode information when the one prediction mode is the onefixed prediction mode.

For example, it is possible that the predicted value is an average valueof the one or more items of attribute information of the one or moresecond three-dimensional points, when a value indicated by theprediction mode information is 0.

For example, it is possible that the first attribute information iscolor information indicating color of a three-dimensional point, andthat the second attribute information is information indicating areflectance of a three-dimensional point.

In accordance with another aspect of the present disclosure, athree-dimensional data decoding method of decoding three-dimensionalpoints includes: obtaining a prediction residual and prediction modeinformation indicating a prediction mode for a first three-dimensionalpoint among the three-dimensional points, by obtaining a bitstream;calculating a predicted value in the prediction mode indicated by theprediction mode information; and calculating attribute information ofthe first three-dimensional point by adding the predicted value to theprediction residual, wherein the prediction mode is: one prediction modeamong two or more prediction modes when a type of the attributeinformation of the first three-dimensional point is first attributeinformation including elements a total number of which is greater than apredetermined threshold value; and one fixed prediction mode when thetype of the attribute information of the first three-dimensional pointis second attribute information including elements a total number ofwhich is smaller than or equal to the predetermined threshold value.

By the above-described three-dimensional data decoding method, it ispossible to appropriately decode attribute information that has beenencoded using the predicted value in the prediction mode indicated bythe bitstream.

For example, it is possible that the bitstream indicates whether or notthe one prediction mode is the one fixed prediction mode, and that thecalculating of the predicted value is performed: in the prediction modeindicated in the prediction mode information when the bitstreamindicates that the one prediction mode is different from the one fixedprediction mode; and in the one fixed prediction mode when the bitstreamindicates that the one prediction mode is the one fixed prediction mode.

For example, it is possible that the predetermined threshold value is 1.

For example, it is possible that the calculating of the predicted valueis performed in the one fixed prediction mode when the bitstream doesnot include the prediction mode information.

For example, it is possible that the predicted value is an average valueof one or more items of attribute information of one or more secondthree-dimensional points in a vicinity of the first three-dimensionalpoint, when a value indicated by the prediction mode information is 0.

For example, it is possible that the first attribute information iscolor information indicating color of a three-dimensional point, andthat the second attribute information is information indicating areflectance of a three-dimensional point.

In accordance with still another aspect of the present disclosure, athree-dimensional data encoding device that encodes three-dimensionalpoints includes: a processor; and memory, wherein using the memory, theprocessor: calculates a predicted value of attribute information of afirst three-dimensional point in a prediction mode for calculating thepredicted value, using one or more items of attribute information of oneor more second three-dimensional points in a vicinity of the firstthree-dimensional point, the first three-dimensional point and the oneor more second three-dimensional points being included in thethree-dimensional points; calculates a prediction residual that is adifference between the attribute information of the firstthree-dimensional point and the predicted value calculated; andgenerates a bitstream including the prediction residual and predictionmode information indicating the prediction mode, wherein the predictionmode is: one prediction mode among two or more prediction modes when atype of the attribute information of the first three-dimensional pointis first attribute information including elements a total number ofwhich is greater than a predetermined threshold value; and one fixedprediction mode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.

The above-described three-dimensional data encoding device can encodeattribute information including many elements by one prediction modeselected from two or more prediction modes, thereby reducing overheadand increasing the encoding efficiency of the attribute information.

In accordance with still another aspect of the present disclosure, athree-dimensional data decoding device that decodes three-dimensionalpoints includes: a processor; and memory, wherein using the memory, theprocessor: obtains a prediction residual and prediction mode informationindicating a prediction mode for a first three-dimensional point amongthe three-dimensional points, by obtaining a bitstream; calculates apredicted value in the prediction mode indicated by the prediction modeinformation; and calculates attribute information of the firstthree-dimensional point by adding the predicted value to the predictionresidual, wherein the prediction mode is: one prediction mode among twoor more prediction modes when a type of the attribute information of thefirst three-dimensional point is first attribute information includingelements a total number of which is greater than a predeterminedthreshold value; and one fixed prediction mode when the type of theattribute information of the first three-dimensional point is secondattribute information including elements a total number of which issmaller than or equal to the predetermined threshold value.

The above-described three-dimensional data decoding device canappropriately decode attribute information that has been encoded usingthe predicted value in the prediction mode indicated by the bitstream.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a CD-ROM, ormay be implemented as any combination of a system, a method, anintegrated circuit, a computer program, and a recording medium.

Hereinafter, embodiments will be specifically described with referenceto the drawings. It is to be noted that each of the followingembodiments indicate a specific example of the present disclosure. Thenumerical values, shapes, materials, constituent elements, thearrangement and connection of the constituent elements, steps, theprocessing order of the steps, etc., indicated in the followingembodiments are mere examples, and thus are not intended to limit thepresent disclosure. Among the constituent elements described in thefollowing embodiments, constituent elements not recited in any one ofthe independent claims will be described as optional constituentelements.

Embodiment 1

When using encoded data of a point cloud in a device or for a service inpractice, required information for the application is desirablytransmitted and received in order to reduce the network bandwidth.However, conventional encoding structures for three-dimensional datahave no such a function, and there is also no encoding method for such afunction.

Embodiment 1 described below relates to a three-dimensional dataencoding method and a three-dimensional data encoding device for encodeddata of a three-dimensional point cloud that provides a function oftransmitting and receiving required information for an application, athree-dimensional data decoding method and a three-dimensional datadecoding device for decoding the encoded data, a three-dimensional datamultiplexing method for multiplexing the encoded data, and athree-dimensional data transmission method for transmitting the encodeddata.

In particular, at present, a first encoding method and a second encodingmethod are under investigation as encoding methods (encoding schemes)for point cloud data. However, there is no method defined for storingthe configuration of encoded data and the encoded data in a systemformat. Thus, there is a problem that an encoder cannot perform an MUXprocess (multiplexing), transmission, or accumulation of data.

In addition, there is no method for supporting a format that involvestwo codecs, the first encoding method and the second encoding method,such as point cloud compression (PCC).

With regard to this embodiment, a configuration of PCC-encoded data thatinvolves two codecs, a first encoding method and a second encodingmethod, and a method of storing the encoded data in a system format willbe described.

A configuration of a three-dimensional data (point cloud data) encodingand decoding system according to this embodiment will be firstdescribed. FIG. 1 is a diagram showing an example of a configuration ofthe three-dimensional data encoding and decoding system according tothis embodiment. As shown in FIG. 1 , the three-dimensional dataencoding and decoding system includes three-dimensional data encodingsystem 4601, three-dimensional data decoding system 4602, sensorterminal 4603, and external connector 4604.

Three-dimensional data encoding system 4601 generates encoded data ormultiplexed data by encoding point cloud data, which isthree-dimensional data. Three-dimensional data encoding system 4601 maybe a three-dimensional data encoding device implemented by a singledevice or a system implemented by a plurality of devices. Thethree-dimensional data encoding device may include a part of a pluralityof processors included in three-dimensional data encoding system 4601.

Three-dimensional data encoding system 4601 includes point cloud datageneration system 4611, presenter 4612, encoder 4613, multiplexer 4614,input/output unit 4615, and controller 4616. Point cloud data generationsystem 4611 includes sensor information obtainer 4617, and point clouddata generator 4618.

Sensor information obtainer 4617 obtains sensor information from sensorterminal 4603, and outputs the sensor information to point cloud datagenerator 4618. Point cloud data generator 4618 generates point clouddata from the sensor information, and outputs the point cloud data toencoder 4613.

Presenter 4612 presents the sensor information or point cloud data to auser. For example, presenter 4612 displays information or an image basedon the sensor information or point cloud data.

Encoder 4613 encodes (compresses) the point cloud data, and outputs theresulting encoded data, control information (signaling information)obtained in the course of the encoding, and other additional informationto multiplexer 4614. The additional information includes the sensorinformation, for example.

Multiplexer 4614 generates multiplexed data by multiplexing the encodeddata, the control information, and the additional information inputthereto from encoder 4613. A format of the multiplexed data is a fileformat for accumulation or a packet format for transmission, forexample.

Input/output unit 4615 (a communication unit or interface, for example)outputs the multiplexed data to the outside. Alternatively, themultiplexed data may be accumulated in an accumulator, such as aninternal memory. Controller 4616 (or an application executor) controlseach processor. That is, controller 4616 controls the encoding, themultiplexing, or other processing.

Note that the sensor information may be input to encoder 4613 ormultiplexer 4614. Alternatively, input/output unit 4615 may output thepoint cloud data or encoded data to the outside as it is.

A transmission signal (multiplexed data) output from three-dimensionaldata encoding system 4601 is input to three-dimensional data decodingsystem 4602 via external connector 4604.

Three-dimensional data decoding system 4602 generates point cloud data,which is three-dimensional data, by decoding the encoded data ormultiplexed data. Note that three-dimensional data decoding system 4602may be a three-dimensional data decoding device implemented by a singledevice or a system implemented by a plurality of devices. Thethree-dimensional data decoding device may include a part of a pluralityof processors included in three-dimensional data decoding system 4602.

Three-dimensional data decoding system 4602 includes sensor informationobtainer 4621, input/output unit 4622, demultiplexer 4623, decoder 4624,presenter 4625, user interface 4626, and controller 4627.

Sensor information obtainer 4621 obtains sensor information from sensorterminal 4603.

Input/output unit 4622 obtains the transmission signal, decodes thetransmission signal into the multiplexed data (file format or packet),and outputs the multiplexed data to demultiplexer 4623.

Demultiplexer 4623 obtains the encoded data, the control information,and the additional information from the multiplexed data, and outputsthe encoded data, the control information, and the additionalinformation to decoder 4624.

Decoder 4624 reconstructs the point cloud data by decoding the encodeddata.

Presenter 4625 presents the point cloud data to a user. For example,presenter 4625 displays information or an image based on the point clouddata. User interface 4626 obtains an indication based on a manipulationby the user. Controller 4627 (or an application executor) controls eachprocessor. That is, controller 4627 controls the demultiplexing, thedecoding, the presentation, or other processing.

Note that input/output unit 4622 may obtain the point cloud data orencoded data as it is from the outside. Presenter 4625 may obtainadditional information, such as sensor information, and presentinformation based on the additional information. Presenter 4625 mayperform a presentation based on an indication from a user obtained onuser interface 4626.

Sensor terminal 4603 generates sensor information, which is informationobtained by a sensor. Sensor terminal 4603 is a terminal provided with asensor or a camera. For example, sensor terminal 4603 is a mobile body,such as an automobile, a flying object, such as an aircraft, a mobileterminal, or a camera.

Sensor information that can be generated by sensor terminal 4603includes (1) the distance between sensor terminal 4603 and an object orthe reflectance of the object obtained by LiDAR, a millimeter waveradar, or an infrared sensor or (2) the distance between a camera and anobject or the reflectance of the object obtained by a plurality ofmonocular camera images or a stereo-camera image, for example. Thesensor information may include the posture, orientation, gyro (angularvelocity), position (GPS information or altitude), velocity, oracceleration of the sensor, for example. The sensor information mayinclude air temperature, air pressure, air humidity, or magnetism, forexample.

External connector 4604 is implemented by an integrated circuit (LSI orIC), an external accumulator, communication with a cloud server via theInternet, or broadcasting, for example.

Next, point cloud data will be described. FIG. 2 is a diagram showing aconfiguration of point cloud data. FIG. 3 is a diagram showing aconfiguration example of a data file describing information of the pointcloud data.

Point cloud data includes data on a plurality of points. Data on eachpoint includes geometry information (three-dimensional coordinates) andattribute information associated with the geometry information. A set ofa plurality of such points is referred to as a point cloud. For example,a point cloud indicates a three-dimensional shape of an object.

Geometry information (position), such as three-dimensional coordinates,may be referred to as geometry. Data on each point may include attributeinformation (attribute) on a plurality of types of attributes. A type ofattribute is color or reflectance, for example.

One item of attribute information (in other words, a piece of attributeinformation or an attribute information item) may be associated with oneitem of geometry information (in other words, a piece of geometryinformation or a geometry information item), or attribute information ona plurality of different types of attributes may be associated with oneitem of geometry information. Alternatively, items of attributeinformation on the same type of attribute may be associated with oneitem of geometry information.

The configuration example of a data file shown in FIG. 3 is an examplein which geometry information and attribute information are associatedwith each other in a one-to-one relationship, and geometry informationand attribute information on N points forming point cloud data areshown.

The geometry information is information on three axes, specifically, anx-axis, a y-axis, and a z-axis, for example. The attribute informationis RGB color information, for example. A representative data file is plyfile, for example.

Next, types of point cloud data will be described. FIG. 4 is a diagramshowing types of point cloud data. As shown in FIG. 4 , point cloud dataincludes a static object and a dynamic object.

The static object is three-dimensional point cloud data at an arbitrarytime (a time point). The dynamic object is three-dimensional point clouddata that varies with time. In the following, three-dimensional pointcloud data associated with a time point will be referred to as a PCCframe or a frame.

The object may be a point cloud whose range is limited to some extent,such as ordinary video data, or may be a large point cloud whose rangeis not limited, such as map information.

There are point cloud data having varying densities. There may be sparsepoint cloud data and dense point cloud data.

In the following, each processor will be described in detail. Sensorinformation is obtained by various means, including a distance sensorsuch as LiDAR or a range finder, a stereo camera, or a combination of aplurality of monocular cameras. Point cloud data generator 4618generates point cloud data based on the sensor information obtained bysensor information obtainer 4617. Point cloud data generator 4618generates geometry information as point cloud data, and adds attributeinformation associated with the geometry information to the geometryinformation.

When generating geometry information or adding attribute information,point cloud data generator 4618 may process the point cloud data. Forexample, point cloud data generator 4618 may reduce the data amount byomitting a point cloud whose position coincides with the position ofanother point cloud. Point cloud data generator 4618 may also convertthe geometry information (such as shifting, rotating or normalizing theposition) or render the attribute information.

Note that, although FIG. 1 shows point cloud data generation system 4611as being included in three-dimensional data encoding system 4601, pointcloud data generation system 4611 may be independently provided outsidethree-dimensional data encoding system 4601.

Encoder 4613 generates encoded data by encoding point cloud dataaccording to an encoding method previously defined. In general, thereare the two types of encoding methods described below. One is anencoding method using geometry information, which will be referred to asa first encoding method, hereinafter. The other is an encoding methodusing a video codec, which will be referred to as a second encodingmethod, hereinafter.

Decoder 4624 decodes the encoded data into the point cloud data usingthe encoding method previously defined.

Multiplexer 4614 generates multiplexed data by multiplexing the encodeddata in an existing multiplexing method. The generated multiplexed datais transmitted or accumulated. Multiplexer 4614 multiplexes not only thePCC-encoded data but also another medium, such as a video, an audio,subtitles, an application, or a file, or reference time information.Multiplexer 4614 may further multiplex attribute information associatedwith sensor information or point cloud data.

Multiplexing schemes or file formats include ISOBMFF, MPEG-DASH, whichis a transmission scheme based on ISOBMFF, MMT, MPEG-2 TS Systems, orRMP, for example.

Demultiplexer 4623 extracts PCC-encoded data, other media, timeinformation and the like from the multiplexed data.

Input/output unit 4615 transmits the multiplexed data in a methodsuitable for the transmission medium or accumulation medium, such asbroadcasting or communication. Input/output unit 4615 may communicatewith another device over the Internet or communicate with anaccumulator, such as a cloud server.

As a communication protocol, http, ftp, TCP, UDP or the like is used.The pull communication scheme or the push communication scheme can beused.

A wired transmission or a wireless transmission can be used. For thewired transmission, Ethernet (registered trademark), USB, RS-232C, HDMI(registered trademark), or a coaxial cable is used, for example. For thewireless transmission, wireless LAN, Wi-Fi (registered trademark),Bluetooth (registered trademark), or a millimeter wave is used, forexample.

As a broadcasting scheme, DVB-T2, DVB-S2, DVB-C2, ATSC3.0, or ISDB-S3 isused, for example.

FIG. 5 is a diagram showing a configuration of first encoder 4630, whichis an example of encoder 4613 that performs encoding in the firstencoding method. FIG. 6 is a block diagram showing first encoder 4630.First encoder 4630 generates encoded data (encoded stream) by encodingpoint cloud data in the first encoding method. First encoder 4630includes geometry information encoder 4631, attribute informationencoder 4632, additional information encoder 4633, and multiplexer 4634.

First encoder 4630 is characterized by performing encoding by keeping athree-dimensional structure in mind. First encoder 4630 is furthercharacterized in that attribute information encoder 4632 performsencoding using information obtained from geometry information encoder4631. The first encoding method is referred to also as geometry-basedPCC (GPCC).

Point cloud data is PCC point cloud data like a PLY file or PCC pointcloud data generated from sensor information, and includes geometryinformation (position), attribute information (attribute), and otheradditional information (metadata). The geometry information is input togeometry information encoder 4631, the attribute information is input toattribute information encoder 4632, and the additional information isinput to additional information encoder 4633.

Geometry information encoder 4631 generates encoded geometry information(compressed geometry), which is encoded data, by encoding geometryinformation. For example, geometry information encoder 4631 encodesgeometry information using an N-ary tree structure, such as an octree.Specifically, in the case of an octree, a current space (target space)is divided into eight nodes (subspaces), 8-bit information (occupancycode) that indicates whether each node includes a point cloud or not isgenerated. A node including a point cloud is further divided into eightnodes, and 8-bit information that indicates whether each of the eightnodes includes a point cloud or not is generated. This process isrepeated until a predetermined level is reached or the number of thepoint clouds included in each node becomes equal to or less than athreshold.

Attribute information encoder 4632 generates encoded attributeinformation (compressed attribute), which is encoded data, by encodingattribute information using configuration information generated bygeometry information encoder 4631. For example, attribute informationencoder 4632 determines a reference point (reference node) that is to bereferred to in encoding a current point (in other words, a current nodeor a target node) to be processed based on the octree structuregenerated by geometry information encoder 4631. For example, attributeinformation encoder 4632 refers to a node whose parent node in theoctree is the same as the parent node of the current node, of peripheralnodes or neighboring nodes. Note that the method of determining areference relationship is not limited to this method.

The process of encoding attribute information may include at least oneof a quantization process, a prediction process, and an arithmeticencoding process. In this case, “refer to” means using a reference nodefor calculating a predicted value of attribute information or using astate of a reference node (occupancy information that indicates whethera reference node includes a point cloud or not, for example) fordetermining a parameter of encoding. For example, the parameter ofencoding is a quantization parameter in the quantization process or acontext or the like in the arithmetic encoding.

Additional information encoder 4633 generates encoded additionalinformation (compressed metadata), which is encoded data, by encodingcompressible data of additional information.

Multiplexer 4634 generates encoded stream (compressed stream), which isencoded data, by multiplexing encoded geometry information, encodedattribute information, encoded additional information, and otheradditional information. The generated encoded stream is output to aprocessor in a system layer (not shown).

Next, first decoder 4640, which is an example of decoder 4624 thatperforms decoding in the first encoding method, will be described. FIG.7 is a diagram showing a configuration of first decoder 4640. FIG. 8 isa block diagram showing first decoder 4640. First decoder 4640 generatespoint cloud data by decoding encoded data (encoded stream) encoded inthe first encoding method in the first encoding method. First decoder4640 includes demultiplexer 4641, geometry information decoder 4642,attribute information decoder 4643, and additional information decoder4644.

An encoded stream (compressed stream), which is encoded data, is inputto first decoder 4640 from a processor in a system layer (not shown).

Demultiplexer 4641 separates encoded geometry information (compressedgeometry), encoded attribute information (compressed attribute), encodedadditional information (compressed metadata), and other additionalinformation from the encoded data.

Geometry information decoder 4642 generates geometry information bydecoding the encoded geometry information. For example, geometryinformation decoder 4642 restores the geometry information on a pointcloud represented by three-dimensional coordinates from encoded geometryinformation represented by an N-ary structure, such as an octree.

Attribute information decoder 4643 decodes the encoded attributeinformation based on configuration information generated by geometryinformation decoder 4642. For example, attribute information decoder4643 determines a reference point (reference node) that is to bereferred to in decoding a current point (current node) to be processedbased on the octree structure generated by geometry information decoder4642. For example, attribute information decoder 4643 refers to a nodewhose parent node in the octree is the same as the parent node of thecurrent node, of peripheral nodes or neighboring nodes. Note that themethod of determining a reference relationship is not limited to thismethod.

The process of decoding attribute information may include at least oneof an inverse quantization process, a prediction process, and anarithmetic decoding process. In this case, “refer to” means using areference node for calculating a predicted value of attributeinformation or using a state of a reference node (occupancy informationthat indicates whether a reference node includes a point cloud or not,for example) for determining a parameter of decoding. For example, theparameter of decoding is a quantization parameter in the inversequantization process or a context or the like in the arithmeticdecoding.

Additional information decoder 4644 generates additional information bydecoding the encoded additional information. First decoder 4640 usesadditional information required for the decoding process for thegeometry information and the attribute information in the decoding, andoutputs additional information required for an application to theoutside.

Next, an example configuration of a geometry information encoder will bedescribed. FIG. 9 is a block diagram of geometry information encoder2700 according to this embodiment. Geometry information encoder 2700includes octree generator 2701, geometry information calculator 2702,encoding table selector 2703, and entropy encoder 2704.

Octree generator 2701 generates an octree, for example, from inputposition information, and generates an occupancy code of each node ofthe octree. Geometry information calculator 2702 obtains informationthat indicates whether a neighboring node of a current node (targetnode) is an occupied node or not. For example, geometry informationcalculator 2702 calculates occupancy information on a neighboring nodefrom an occupancy code of a parent node to which a current node belongs(information that indicates whether a neighboring node is an occupiednode or not). Geometry information calculator 2702 may save an encodednode in a list and search the list for a neighboring node. Note thatgeometry information calculator 2702 may change neighboring nodes inaccordance with the position of the current node in the parent node.

Encoding table selector 2703 selects an encoding table used for entropyencoding of the current node based on the occupancy information on theneighboring node calculated by geometry information calculator 2702. Forexample, encoding table selector 2703 may generate a bit sequence basedon the occupancy information on the neighboring node and select anencoding table of an index number generated from the bit sequence.

Entropy encoder 2704 generates encoded geometry information and metadataby entropy-encoding the occupancy code of the current node using theencoding table of the selected index number. Entropy encoder may add, tothe encoded geometry information, information that indicates theselected encoding table.

In the following, an octree representation and a scan order for geometryinformation will be described. Geometry information (geometry data) istransformed into an octree structure (octree transform) and thenencoded. The octree structure includes nodes and leaves. Each node haseight nodes or leaves, and each leaf has voxel (VXL) information. FIG.10 is a diagram showing an example structure of geometry informationincluding a plurality of voxels. FIG. 11 is a diagram showing an examplein which the geometry information shown in FIG. 10 is transformed intoan octree structure. Here, of leaves shown in FIG. 11 , leaves 1, 2, and3 represent voxels VXL1, VXL2, and VXL3 shown in FIG. 10 , respectively,and each represent VXL containing a point cloud (referred to as a validVXL, hereinafter).

Specifically, node 1 corresponds to the entire space comprising thegeometry information in FIG. 10 . The entire space corresponding to node1 is divided into eight nodes, and among the eight nodes, a nodecontaining valid VXL is further divided into eight nodes or leaves. Thisprocess is repeated for every layer of the tree structure. Here, eachnode corresponds to a subspace, and has information (occupancy code)that indicates where the next node or leaf is located after division asnode information. A block in the bottom layer is designated as a leafand retains the number of the points contained in the leaf as leafinformation.

Next, an example configuration of a geometry information decoder will bedescribed. FIG. 12 is a block diagram of geometry information decoder2710 according to this embodiment. Geometry information decoder 2710includes octree generator 2711, geometry information calculator 2712,encoding table selector 2713, and entropy decoder 2714.

Octree generator 2711 generates an octree of a space (node) based onheader information, metadata or the like of a bitstream. For example,octree generator 2711 generates an octree by generating a large space(root node) based on the sizes of a space in an x-axis direction, ay-axis direction, and a z-axis direction added to the header informationand dividing the space into two parts in the x-axis direction, they-axis direction, and the z-axis direction to generate eight smallspaces A (nodes A0 to A7). Nodes A0 to A7 are sequentially designated asa current node.

Geometry information calculator 2712 obtains occupancy information thatindicates whether a neighboring node of a current node is an occupiednode or not. For example, geometry information calculator 2712calculates occupancy information on a neighboring node from an occupancycode of a parent node to which a current node belongs. Geometryinformation calculator 2712 may save a decoded node in a list and searchthe list for a neighboring node. Note that geometry informationcalculator 2712 may change neighboring nodes in accordance with theposition of the current node in the parent node.

Encoding table selector 2713 selects an encoding table (decoding table)used for entropy decoding of the current node based on the occupancyinformation on the neighboring node calculated by geometry informationcalculator 2712. For example, encoding table selector 2713 may generatea bit sequence based on the occupancy information on the neighboringnode and select an encoding table of an index number generated from thebit sequence.

Entropy decoder 2714 generates position information by entropy-decodingthe occupancy code of the current node using the selected encodingtable. Note that entropy decoder 2714 may obtain information on theselected encoding table by decoding the bitstream, and entropy-decodethe occupancy code of the current node using the encoding tableindicated by the information.

In the following, configurations of an attribute information encoder andan attribute information decoder will be described. FIG. 13 is a blockdiagram showing an example configuration of attribute informationencoder A100. The attribute information encoder may include a pluralityof encoders that perform different encoding methods. For example, theattribute information encoder may selectively use any of the two methodsdescribed below in accordance with the use case.

Attribute information encoder A100 includes LoD attribute informationencoder A101 and transformed-attribute-information encoder A102. LoDattribute information encoder A101 classifies three-dimensional pointsinto a plurality of layers based on geometry information on thethree-dimensional points, predicts attribute information onthree-dimensional points belonging to each layer, and encodes aprediction residual therefor. Here, each layer into which athree-dimensional point is classified is referred to as a level ofdetail (LoD).

Transformed-attribute-information encoder A102 encodes attributeinformation using region adaptive hierarchical transform (RAHT).Specifically, transformed-attribute-information encoder A102 generates ahigh frequency component and a low frequency component for each layer byapplying RAHT or Haar transform to each item of attribute informationbased on the geometry information on three-dimensional points, andencodes the values by quantization, entropy encoding or the like.

FIG. 14 is a block diagram showing an example configuration of attributeinformation decoder A110. The attribute information decoder may includea plurality of decoders that perform different decoding methods. Forexample, the attribute information decoder may selectively use any ofthe two methods described below for decoding based on the informationincluded in the header or metadata.

Attribute information decoder A110 includes LoD attribute informationdecoder A111 and transformed-attribute-information decoder A112. LoDattribute information decoder A111 classifies three-dimensional pointsinto a plurality of layers based on the geometry information on thethree-dimensional points, predicts attribute information onthree-dimensional points belonging to each layer, and decodes attributevalues thereof.

Transformed-attribute-information decoder A112 decodes attributeinformation using region adaptive hierarchical transform (RAHT).Specifically, transformed-attribute-information decoder A112 decodeseach attribute value by applying inverse RAHT or inverse Haar transformto the high frequency component and the low frequency component of theattribute value based on the geometry information on thethree-dimensional point.

FIG. 15 is a block diagram showing a configuration of attributeinformation encoder 3140 that is an example of LoD attribute informationencoder A101.

Attribute information encoder 3140 includes LoD generator 3141,periphery searcher 3142, predictor 3143, prediction residual calculator3144, quantizer 3145, arithmetic encoder 3146, inverse quantizer 3147,decoded value generator 3148, and memory 3149.

LoD generator 3141 generates an LoD using geometry information on athree-dimensional point.

Periphery searcher 3142 searches for a neighboring three-dimensionalpoint neighboring each three-dimensional point using a result of LoDgeneration by LoD generator 3141 and distance information indicatingdistances between three-dimensional points.

Predictor 3143 generates a predicted value of an item of attributeinformation on a current (target) three-dimensional point to be encoded.

Prediction residual calculator 3144 calculates (generates) a predictionresidual of the predicted value of the item of the attribute informationgenerated by predictor 3143.

Quantizer 3145 quantizes the prediction residual of the item ofattribute information calculated by prediction residual calculator 3144.

Arithmetic encoder 3146 arithmetically encodes the prediction residualquantized by quantizer 3145. Arithmetic encoder 3146 outputs a bitstreamincluding the arithmetically encoded prediction residual to thethree-dimensional data decoding device, for example.

The prediction residual may be binarized by quantizer 3145 before beingarithmetically encoded by arithmetic encoder 3146.

Arithmetic encoder 3146 may initialize the encoding table used for thearithmetic encoding before performing the arithmetic encoding.Arithmetic encoder 3146 may initialize the encoding table used for thearithmetic encoding for each layer. Arithmetic encoder 3146 may output abitstream including information that indicates the position of the layerat which the encoding table is initialized.

Inverse quantizer 3147 inverse-quantizes the prediction residualquantized by quantizer 3145.

Decoded value generator 3148 generates a decoded value by adding thepredicted value of the item of attribute information generated bypredictor 3143 and the prediction residual inverse-quantized by inversequantizer 3147 together.

Memory 3149 is a memory that stores a decoded value of an item ofattribute information on each three-dimensional point decoded by decodedvalue generator 3148. For example, when generating a predicted value ofa three-dimensional point yet to be encoded, predictor 3143 may generatethe predicted value using a decoded value of an item of attributeinformation on each three-dimensional point stored in memory 3149.

FIG. 16 is a block diagram of attribute information encoder 6600 that isan example of transformation attribute information encoder A102.Attribute information encoder 6600 includes sorter 6601, Haartransformer 6602, quantizer 6603, inverse quantizer 6604, inverse Haartransformer 6605, memory 6606, and arithmetic encoder 6607.

Sorter 6601 generates the Morton codes by using the geometry informationof three-dimensional points, and sorts the plurality ofthree-dimensional points in the order of the Morton codes. Haartransformer 6602 generates the coding coefficient by applying the Haartransform to the attribute information. Quantizer 6603 quantizes thecoding coefficient of the attribute information.

Inverse quantizer 6604 inverse quantizes the coding coefficient afterthe quantization. Inverse Haar transformer 6605 applies the inverse Haartransform to the coding coefficient. Memory 6606 stores the values ofitems of attribute information of a plurality of decodedthree-dimensional points. For example, the attribute information of thedecoded three-dimensional points stored in memory 6606 may be utilizedfor prediction and the like of an unencoded three-dimensional point.

Arithmetic encoder 6607 calculates ZeroCnt from the coding coefficientafter the quantization, and arithmetically encodes ZeroCnt.Additionally, arithmetic encoder 6607 arithmetically encodes thenon-zero coding coefficient after the quantization. Arithmetic encoder6607 may binarize the coding coefficient before the arithmetic encoding.In addition, arithmetic encoder 6607 may generate and encode variouskinds of header information.

FIG. 17 is a block diagram showing a configuration of attributeinformation decoder 3150 that is an example of LoD attribute informationdecoder A111.

Attribute information decoder 3150 includes LoD generator 3151,periphery searcher 3152, predictor 3153, arithmetic decoder 3154,inverse quantizer 3155, decoded value generator 3156, and memory 3157.

LoD generator 3151 generates an LoD using geometry information on athree-dimensional point decoded by the geometry information decoder (notshown in FIG. 17 ).

Periphery searcher 3152 searches for a neighboring three-dimensionalpoint neighboring each three-dimensional point using a result of LoDgeneration by LoD generator 3151 and distance information indicatingdistances between three-dimensional points.

Predictor 3153 generates a predicted value of attribute information itemon a current three-dimensional point to be decoded.

Arithmetic decoder 3154 arithmetically decodes the prediction residualin the bitstream obtained from attribute information encoder 3140 shownin FIG. 15 . Note that arithmetic decoder 3154 may initialize thedecoding table used for the arithmetic decoding. Arithmetic decoder 3154initializes the decoding table used for the arithmetic decoding for thelayer for which the encoding process has been performed by arithmeticencoder 3146 shown in FIG. 15 . Arithmetic decoder 3154 may initializethe decoding table used for the arithmetic decoding for each layer.Arithmetic decoder 3154 may initialize the decoding table based on theinformation included in the bitstream that indicates the position of thelayer for which the encoding table has been initialized.

Inverse quantizer 3155 inverse-quantizes the prediction residualarithmetically decoded by arithmetic decoder 3154.

Decoded value generator 3156 generates a decoded value by adding thepredicted value generated by predictor 3153 and the prediction residualinverse-quantized by inverse quantizer 3155 together. Decoded valuegenerator 3156 outputs the decoded attribute information data to anotherdevice.

Memory 3157 is a memory that stores a decoded value of an item ofattribute information on each three-dimensional point decoded by decodedvalue generator 3156. For example, when generating a predicted value ofa three-dimensional point yet to be decoded, predictor 3153 generatesthe predicted value using a decoded value of an item of attributeinformation on each three-dimensional point stored in memory 3157.

FIG. 18 is a block diagram of attribute information decoder 6610 that isan example of transformation attribute information decoder A112.Attribute information decoder 6610 includes arithmetic decoder 6611,inverse quantizer 6612, inverse Haar transformer 6613, and memory 6614.

Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the codingcoefficient included in a bitstream. Note that arithmetic decoder 6611may decode various kinds of header information.

Inverse quantizer 6612 inverse quantizes the arithmetically decodedcoding coefficient. Inverse Haar transformer 6613 applies the inverseHaar transform to the coding coefficient after the inverse quantization.Memory 6614 stores the values of items of attribute information of aplurality of decoded three-dimensional points. For example, theattribute information of the decoded three-dimensional points stored inmemory 6614 may be utilized for prediction of an undecodedthree-dimensional point.

Next, second encoder 4650, which is an example of encoder 4613 thatperforms encoding in the second encoding method, will be described. FIG.19 is a diagram showing a configuration of second encoder 4650. FIG. 20is a block diagram showing second encoder 4650.

Second encoder 4650 generates encoded data (encoded stream) by encodingpoint cloud data in the second encoding method. Second encoder 4650includes additional information generator 4651, geometry image generator4652, attribute image generator 4653, video encoder 4654, additionalinformation encoder 4655, and multiplexer 4656.

Second encoder 4650 is characterized by generating a geometry image andan attribute image by projecting a three-dimensional structure onto atwo-dimensional image, and encoding the generated geometry image andattribute image in an existing video encoding scheme. The secondencoding method is referred to as video-based PCC (VPCC).

Point cloud data is PCC point cloud data like a PLY file or PCC pointcloud data generated from sensor information, and includes geometryinformation (position), attribute information (attribute), and otheradditional information (metadata).

Additional information generator 4651 generates map information on aplurality of two-dimensional images by projecting a three-dimensionalstructure onto a two-dimensional image.

Geometry image generator 4652 generates a geometry image based on thegeometry information and the map information generated by additionalinformation generator 4651. The geometry image is a distance image inwhich distance (depth) is indicated as a pixel value, for example. Thedistance image may be an image of a plurality of point clouds viewedfrom one point of view (an image of a plurality of point cloudsprojected onto one two-dimensional plane), a plurality of images of aplurality of point clouds viewed from a plurality of points of view, ora single image integrating the plurality of images.

Attribute image generator 4653 generates an attribute image based on theattribute information and the map information generated by additionalinformation generator 4651. The attribute image is an image in whichattribute information (color (RGB), for example) is indicated as a pixelvalue, for example. The image may be an image of a plurality of pointclouds viewed from one point of view (an image of a plurality of pointclouds projected onto one two-dimensional plane), a plurality of imagesof a plurality of point clouds viewed from a plurality of points ofview, or a single image integrating the plurality of images.

Video encoder 4654 generates an encoded geometry image (compressedgeometry image) and an encoded attribute image (compressed attributeimage), which are encoded data, by encoding the geometry image and theattribute image in a video encoding scheme. Note that, as the videoencoding scheme, any well-known encoding method can be used. Forexample, the video encoding scheme is AVC or HEVC.

Additional information encoder 4655 generates encoded additionalinformation (compressed metadata) by encoding the additionalinformation, the map information and the like included in the pointcloud data.

Multiplexer 4656 generates an encoded stream (compressed stream), whichis encoded data, by multiplexing the encoded geometry image, the encodedattribute image, the encoded additional information, and otheradditional information. The generated encoded stream is output to aprocessor in a system layer (not shown).

Next, second decoder 4660, which is an example of decoder 4624 thatperforms decoding in the second encoding method, will be described. FIG.21 is a diagram showing a configuration of second decoder 4660. FIG. 22is a block diagram showing second decoder 4660. Second decoder 4660generates point cloud data by decoding encoded data (encoded stream)encoded in the second encoding method in the second encoding method.Second decoder 4660 includes demultiplexer 4661, video decoder 4662,additional information decoder 4663, geometry information generator4664, and attribute information generator 4665.

An encoded stream (compressed stream), which is encoded data, is inputto second decoder 4660 from a processor in a system layer (not shown).

Demultiplexer 4661 separates an encoded geometry image (compressedgeometry image), an encoded attribute image (compressed attributeimage), an encoded additional information (compressed metadata), andother additional information from the encoded data.

Video decoder 4662 generates a geometry image and an attribute image bydecoding the encoded geometry image and the encoded attribute image in avideo encoding scheme. Note that, as the video encoding scheme, anywell-known encoding method can be used. For example, the video encodingscheme is AVC or HEVC.

Additional information decoder 4663 generates additional informationincluding map information or the like by decoding the encoded additionalinformation.

Geometry information generator 4664 generates geometry information fromthe geometry image and the map information. Attribute informationgenerator 4665 generates attribute information from the attribute imageand the map information.

Second decoder 4660 uses additional information required for decoding inthe decoding, and outputs additional information required for anapplication to the outside.

In the following, a problem with the PCC encoding scheme will bedescribed. FIG. 23 is a diagram showing a protocol stack relating toPCC-encoded data. FIG. 23 shows an example in which PCC-encoded data ismultiplexed with other medium data, such as a video (HEVC, for example)or an audio, and transmitted or accumulated.

A multiplexing scheme and a file format have a function of multiplexingvarious encoded data and transmitting or accumulating the data. Totransmit or accumulate encoded data, the encoded data has to beconverted into a format for the multiplexing scheme. For example, withHEVC, a technique for storing encoded data in a data structure referredto as a NAL unit and storing the NAL unit in ISOBMFF is prescribed.

At present, a first encoding method (Codec1) and a second encodingmethod (Codec2) are under investigation as encoding methods for pointcloud data. However, there is no method defined for storing theconfiguration of encoded data and the encoded data in a system format.Thus, there is a problem that an encoder cannot perform an MUX process(multiplexing), transmission, or accumulation of data.

Note that, in the following, the term “encoding method” means any of thefirst encoding method and the second encoding method unless a particularencoding method is specified.

Embodiment 2

In this embodiment, types of the encoded data (geometry information(geometry), attribute information (attribute), and additionalinformation (metadata)) generated by first encoder 4630 or secondencoder 4650 described above, a method of generating additionalinformation (metadata), and a multiplexing process in the multiplexerwill be described. The additional information (metadata) may be referredto as a parameter set or control information (signaling information).

In this embodiment, the dynamic object (three-dimensional point clouddata that varies with time) described above with reference to FIG. 4will be described, for example. However, the same method can also beused for the static object (three-dimensional point cloud dataassociated with an arbitrary time point).

FIG. 24 is a diagram showing configurations of encoder 4801 andmultiplexer 4802 in a three-dimensional data encoding device accordingto this embodiment. Encoder 4801 corresponds to first encoder 4630 orsecond encoder 4650 described above, for example. Multiplexer 4802corresponds to multiplexer 4634 or 4656 described above.

Encoder 4801 encodes a plurality of PCC (point cloud compression) framesof point cloud data to generate a plurality of pieces of encoded data(multiple compressed data) of geometry information, attributeinformation, and additional information.

Multiplexer 4802 integrates a plurality of types of data (geometryinformation, attribute information, and additional information) into aNAL unit, thereby converting the data into a data configuration thattakes data access in the decoding device into consideration.

FIG. 25 is a diagram showing a configuration example of the encoded datagenerated by encoder 4801. Arrows in the drawing indicate a dependenceinvolved in decoding of the encoded data. The source of an arrow dependson data of the destination of the arrow. That is, the decoding devicedecodes the data of the destination of an arrow, and decodes the data ofthe source of the arrow using the decoded data. In other words, “a firstentity depends on a second entity” means that data of the second entityis referred to (used) in processing (encoding, decoding, or the like) ofdata of the first entity.

First, a process of generating encoded data of geometry information willbe described. Encoder 4801 encodes geometry information of each frame togenerate encoded geometry data (compressed geometry data) for eachframe. The encoded geometry data is denoted by G(i). i denotes a framenumber or a time point of a frame, for example.

Furthermore, encoder 4801 generates a geometry parameter set (GPS(i))for each frame. The geometry parameter set includes a parameter that canbe used for decoding of the encoded geometry data. The encoded geometrydata for each frame depends on an associated geometry parameter set.

The encoded geometry data formed by a plurality of frames is defined asa geometry sequence. Encoder 4801 generates a geometry sequenceparameter set (referred to also as geometry sequence PS or geometry SPS)that stores a parameter commonly used for a decoding process for theplurality of frames in the geometry sequence. The geometry sequencedepends on the geometry SPS.

Next, a process of generating encoded data of attribute information willbe described. Encoder 4801 encodes attribute information of each frameto generate encoded attribute data (compressed attribute data) for eachframe. The encoded attribute data is denoted by A(i). FIG. 25 shows anexample in which there are attribute X and attribute Y, and encodedattribute data for attribute X is denoted by AX(i), and encodedattribute data for attribute Y is denoted by AY(i).

Furthermore, encoder 4801 generates an attribute parameter set (APS(i))for each frame. The attribute parameter set for attribute X is denotedby AXPS(i), and the attribute parameter set for attribute Y is denotedby AYPS(i). The attribute parameter set includes a parameter that can beused for decoding of the encoded attribute information. The encodedattribute data depends on an associated attribute parameter set.

The encoded attribute data formed by a plurality of frames is defined asan attribute sequence. Encoder 4801 generates an attribute sequenceparameter set (referred to also as attribute sequence PS or attributeSPS) that stores a parameter commonly used for a decoding process forthe plurality of frames in the attribute sequence. The attributesequence depends on the attribute SPS.

In the first encoding method, the encoded attribute data depends on theencoded geometry data.

FIG. 25 shows an example in which there are two types of attributeinformation (attribute X and attribute Y). When there are two types ofattribute information, for example, two encoders generate data andmetadata for the two types of attribute information. For example, anattribute sequence is defined for each type of attribute information,and an attribute SPS is generated for each type of attributeinformation.

Note that, although FIG. 25 shows an example in which there is one typeof geometry information, and there are two types of attributeinformation, the present disclosure is not limited thereto. There may beone type of attribute information or three or more types of attributeinformation. In such cases, encoded data can be generated in the samemanner. If the point cloud data has no attribute information, there maybe no attribute information. In such a case, encoder 4801 does not haveto generate a parameter set associated with attribute information.

Next, a process of generating encoded data of additional information(metadata) will be described. Encoder 4801 generates a PCC stream PS(referred to also as PCC stream PS or stream PS), which is a parameterset for the entire PCC stream. Encoder 4801 stores a parameter that canbe commonly used for a decoding process for one or more geometrysequences and one or more attribute sequences in the stream PS. Forexample, the stream PS includes identification information indicatingthe codec for the point cloud data and information indicating analgorithm used for the encoding, for example. The geometry sequence andthe attribute sequence depend on the stream PS.

Next, an access unit and a GOF will be described. In this embodiment,concepts of access unit (AU) and group of frames (GOF) are newlyintroduced.

An access unit is a basic unit for accessing data in decoding, and isformed by one or more pieces of data and one or more pieces of metadata.For example, an access unit is formed by geometry information and one ormore pieces of attribute information associated with a same time point.A GOF is a random access unit, and is formed by one or more accessunits.

Encoder 4801 generates an access unit header (AU header) asidentification information indicating the top of an access unit. Encoder4801 stores a parameter relating to the access unit in the access unitheader. For example, the access unit header includes a configuration ofor information on the encoded data included in the access unit. Theaccess unit header further includes a parameter commonly used for thedata included in the access unit, such as a parameter relating todecoding of the encoded data.

Note that encoder 4801 may generate an access unit delimiter thatincludes no parameter relating to the access unit, instead of the accessunit header. The access unit delimiter is used as identificationinformation indicating the top of the access unit. The decoding deviceidentifies the top of the access unit by detecting the access unitheader or the access unit delimiter.

Next, generation of identification information for the top of a GOF willbe described. As identification information indicating the top of a GOF,encoder 4801 generates a GOF header. Encoder 4801 stores a parameterrelating to the GOF in the GOF header. For example, the GOF headerincludes a configuration of or information on the encoded data includedin the GOF. The GOF header further includes a parameter commonly usedfor the data included in the GOF, such as a parameter relating todecoding of the encoded data.

Note that encoder 4801 may generate a GOF delimiter that includes noparameter relating to the GOF, instead of the GOF header. The GOFdelimiter is used as identification information indicating the top ofthe GOF. The decoding device identifies the top of the GOF by detectingthe GOF header or the GOF delimiter.

In the PCC-encoded data, the access unit is defined as a PCC frame unit,for example. The decoding device accesses a PCC frame based on theidentification information for the top of the access unit.

For example, the GOF is defined as one random access unit. The decodingdevice accesses a random access unit based on the identificationinformation for the top of the GOF. For example, if PCC frames areindependent from each other and can be separately decoded, a PCC framecan be defined as a random access unit.

Note that two or more PCC frames may be assigned to one access unit, anda plurality of random access units may be assigned to one GOF.

Encoder 4801 may define and generate a parameter set or metadata otherthan those described above. For example, encoder 4801 may generatesupplemental enhancement information (SEI) that stores a parameter (anoptional parameter) that is not always used for decoding.

Next, a configuration of encoded data and a method of storing encodeddata in a NAL unit will be described.

For example, a data format is defined for each type of encoded data.FIG. 26 is a diagram showing an example of encoded data and a NAL unit.

For example, as shown in FIG. 26 , encoded data includes a header and apayload. The encoded data may include length information indicating thelength (data amount) of the encoded data, the header, or the payload.The encoded data may include no header.

The header includes identification information for identifying the data,for example. The identification information indicates a data type or aframe number, for example.

The header includes identification information indicating a referencerelationship, for example. The identification information is stored inthe header when there is a dependence relationship between data, forexample, and allows an entity to refer to another entity. For example,the header of the entity to be referred to includes identificationinformation for identifying the data. The header of the referring entityincludes identification information indicating the entity to be referredto.

Note that, when the entity to be referred to or the referring entity canbe identified or determined from other information, the identificationinformation for identifying the data or identification informationindicating the reference relationship can be omitted.

Multiplexer 4802 stores the encoded data in the payload of the NAL unit.The NAL unit header includes pcc_nal_unit_type, which is identificationinformation for the encoded data. FIG. 27 is a diagram showing asemantics example of pcc_nal_unit_type.

As shown in FIG. 27 , when pcc_codec_type is codec 1 (Codec1: firstencoding method), values 0 to 10 of pcc_nal_unit_type are assigned toencoded geometry data (Geometry), encoded attribute X data (AttributeX),encoded attribute Y data (AttributeY), geometry PS (Geom. PS), attributeXPS (AttrX. S), attribute YPS (AttrY. PS), geometry SPS (GeometrySequence PS), attribute X SPS (AttributeX Sequence PS), attribute Y SPS(AttributeY Sequence PS), AU header (AU Header), and GOF header (GOFHeader) in codec 1. Values of 11 and greater are reserved in codec 1.

When pcc_codec_type is codec 2 (Codec2: second encoding method), valuesof 0 to 2 of pcc_nal_unit_type are assigned to data A (DataA), metadataA (MetaDataA), and metadata B (MetaDataB) in the codec. Values of 3 andgreater are reserved in codec 2.

Next, an order of transmission of data will be described. In thefollowing, restrictions on the order of transmission of NAL units willbe described.

Multiplexer 4802 transmits NAL units on a GOF basis or on an AU basis.Multiplexer 4802 arranges the GOF header at the top of a GOF, andarranges the AU header at the top of an AU.

In order to allow the decoding device to decode the next AU and thefollowing AUs even when data is lost because of a packet loss or thelike, multiplexer 4802 may arrange a sequence parameter set (SPS) ineach AU.

When there is a dependence relationship for decoding between encodeddata, the decoding device decodes the data of the entity to be referredto and then decodes the data of the referring entity. In order to allowthe decoding device to perform decoding in the order of receptionwithout rearranging the data, multiplexer 4802 first transmits the dataof the entity to be referred to.

FIG. 28 is a diagram showing examples of the order of transmission ofNAL units. FIG. 28 shows three examples, that is, geometryinformation-first order, parameter-first order, and data-integratedorder.

The geometry information-first order of transmission is an example inwhich information relating to geometry information is transmittedtogether, and information relating to attribute information istransmitted together. In the case of this order of transmission, thetransmission of the information relating to the geometry informationends earlier than the transmission of the information relating to theattribute information.

For example, according to this order of transmission is used, when thedecoding device does not decode attribute information, the decodingdevice may be able to have an idle time since the decoding device canomit decoding of attribute information. When the decoding device isrequired to decode geometry information early, the decoding device maybe able to decode geometry information earlier since the decoding deviceobtains encoded data of the geometry information earlier.

Note that, although in FIG. 28 the attribute X SPS and the attribute YSPS are integrated and shown as the attribute SPS, the attribute X SPSand the attribute Y SPS may be separately arranged.

In the parameter set-first order of transmission, a parameter set isfirst transmitted, and data is then transmitted.

As described above, as far as the restrictions on the order oftransmission of NAL units are met, multiplexer 4802 can transmit NALunits in any order. For example, order identification information may bedefined, and multiplexer 4802 may have a function of transmitting NALunits in a plurality of orders. For example, the order identificationinformation for NAL units is stored in the stream PS.

The three-dimensional data decoding device may perform decoding based onthe order identification information. The three-dimensional datadecoding device may indicate a desired order of transmission to thethree-dimensional data encoding device, and the three-dimensional dataencoding device (multiplexer 4802) may control the order of transmissionaccording to the indicated order of transmission.

Note that multiplexer 4802 can generate encoded data having a pluralityof functions merged to each other as in the case of the data-integratedorder of transmission, as far as the restrictions on the order oftransmission are met. For example, as shown in FIG. 28 , the GOF headerand the AU header may be integrated, or AXPS and AYPS may be integrated.In such a case, an identifier that indicates data having a plurality offunctions is defined in pcc_nal_unit_type.

In the following, variations of this embodiment will be described. Thereare levels of PSs, such as a frame-level PS, a sequence-level PS, and aPCC sequence-level PS. Provided that the PCC sequence level is a higherlevel, and the frame level is a lower level, parameters can be stored inthe manner described below.

The value of a default PS is indicated in a PS at a higher level. If thevalue of a PS at a lower level differs from the value of the PS at ahigher level, the value of the PS is indicated in the PS at the lowerlevel. Alternatively, the value of the PS is not described in the PS atthe higher level but is described in the PS at the lower level.Alternatively, information indicating whether the value of the PS isindicated in the PS at the lower level, at the higher level, or at boththe levels is indicated in both or one of the PS at the lower level andthe PS at the higher level. Alternatively, the PS at the lower level maybe merged with the PS at the higher level. If the PS at the lower leveland the PS at the higher level overlap with each other, multiplexer 4802may omit transmission of one of the PSs.

Note that encoder 4801 or multiplexer 4802 may divide data into slicesor tiles and transmit each of the divided slices or tiles as divideddata. The divided data includes information for identifying the divideddata, and a parameter used for decoding of the divided data is includedin the parameter set. In this case, an identifier that indicates thatthe data is data relating to a tile or slice or data storing a parameteris defined in pcc_nal_unit_type.

In the following, a process relating to order identification informationwill be described. FIG. 29 is a flowchart showing a process performed bythe three-dimensional data encoding device (encoder 4801 and multiplexer4802) that involves the order of transmission of NAL units.

First, the three-dimensional data encoding device determines the orderof transmission of NAL units (geometry information-first or parameterset-first) (S4801). For example, the three-dimensional data encodingdevice determines the order of transmission based on a specificationfrom a user or an external device (the three-dimensional data decodingdevice, for example).

If the determined order of transmission is geometry information-first(if “geometry information-first” in S4802), the three-dimensional dataencoding device sets the order identification information included inthe stream PS to geometry information-first (S4803). That is, in thiscase, the order identification information indicates that the NAL unitsare transmitted in the geometry information-first order. Thethree-dimensional data encoding device then transmits the NAL units inthe geometry information-first order (S4804).

On the other hand, if the determined order of transmission is parameterset-first (if “parameter set-first” in S4802), the three-dimensionaldata encoding device sets the order identification information includedin the stream PS to parameter set-first (S4805). That is, in this case,the order identification information indicates that the NAL units aretransmitted in the parameter set-first order. The three-dimensional dataencoding device then transmits the NAL units in the parameter set-firstorder (S4806).

FIG. 30 is a flowchart showing a process performed by thethree-dimensional data decoding device that involves the order oftransmission of NAL units. First, the three-dimensional data decodingdevice analyzes the order identification information included in thestream PS (S4811).

If the order of transmission indicated by the order identificationinformation is geometry information-first (if “geometryinformation-first” in S4812), the three-dimensional data decoding devicedecodes the NAL units based on the determination that the order oftransmission of the NAL units is geometry information-first (S4813).

On the other hand, if the order of transmission indicated by the orderidentification information is parameter set-first (if “parameterset-first” in S4812), the three-dimensional data decoding device decodesthe NAL units based on the determination that the order of transmissionof the NAL units is parameter set-first (S4814).

For example, if the three-dimensional data decoding device does notdecode attribute information, in step S4813, the three-dimensional datadecoding device does not obtain the entire NAL units but can obtain apart of a NAL unit relating to the geometry information and decode theobtained NAL unit to obtain the geometry information.

Next, a process relating to generation of an AU and a GOF will bedescribed. FIG. 31 is a flowchart showing a process performed by thethree-dimensional data encoding device (multiplexer 4802) that relatesto generation of an AU and a GOF in multiplexing of NAL units.

First, the three-dimensional data encoding device determines the type ofthe encoded data (S4821). Specifically, the three-dimensional dataencoding device determines whether the encoded data to be processed isAU-first data, GOF-first data, or other data.

If the encoded data is GOF-first data (if “GOF-first” in S4822), thethree-dimensional data encoding device generates NAL units by arranginga GOF header and an AU header at the top of the encoded data belongingto the GOF (S4823).

If the encoded data is AU-first data (if “AU-first” in S4822), thethree-dimensional data encoding device generates NAL units by arrangingan AU header at the top of the encoded data belonging to the AU (S4824).

If the encoded data is neither GOF-first data nor AU-first data (if“other than GOF-first and AU-first” in S4822), the three-dimensionaldata encoding device generates NAL units by arranging the encoded datato follow the AU header of the AU to which the encoded data belongs(S4825).

Next, a process relating to access to an AU and a GOF will be described.FIG. 32 is a flowchart showing a process performed by thethree-dimensional data decoding device that involves accessing to an AUand a GOF in demultiplexing of a NAL unit.

First, the three-dimensional data decoding device determines the type ofthe encoded data included in the NAL unit by analyzing nal_unit_type inthe NAL unit (S4831). Specifically, the three-dimensional data decodingdevice determines whether the encoded data included in the NAL unit isAU-first data, GOF-first data, or other data.

If the encoded data included in the NAL unit is GOF-first data (if“GOF-first” in S4832), the three-dimensional data decoding devicedetermines that the NAL unit is a start position of random access,accesses the NAL unit, and starts the decoding process (S4833).

If the encoded data included in the NAL unit is AU-first data (if“AU-first” in S4832), the three-dimensional data decoding devicedetermines that the NAL unit is AU-first, accesses the data included inthe NAL unit, and decodes the AU (S4834).

If the encoded data included in the NAL unit is neither GOF-first datanor AU-first data (if “other than GOF-first and AU-first” in S4832), thethree-dimensional data decoding device does not process the NAL unit.

Embodiment 3

Information of a three-dimensional point cloud includes geometryinformation (geometry) and attribute information (attribute). Geometryinformation includes coordinates (x-coordinate, y-coordinate,z-coordinate) with respect to a certain point. When geometry informationis encoded, a method of representing the position of each ofthree-dimensional points in octree representation and encoding theoctree information to reduce a code amount is used instead of directlyencoding the coordinates of the three-dimensional point.

On the other hand, attribute information includes informationindicating, for example, color information (RGB, YUV, etc.) of eachthree-dimensional point, a reflectance, and a normal vector. Forexample, a three-dimensional data encoding device is capable of encodingattribute information using an encoding method different from a methodused to encode geometry information.

In the present embodiment, a method of encoding attribute information isexplained. It is to be noted that, in the present embodiment, the methodis explained based on an example case using integer values as values ofattribute information. For example, when each of RGB or YUV colorcomponents is of an 8-bit accuracy, the color component is an integervalue in a range from 0 to 255. When a reflectance value is of 10-bitaccuracy, the reflectance value is an integer in a range from 0 to 1023.It is to be noted that, when the bit accuracy of attribute informationis a decimal accuracy, the three-dimensional data encoding device maymultiply the value by a scale value to round it to an integer value sothat the value of the attribute information becomes an integer value. Itis to be noted that the three-dimensional data encoding device may addthe scale value to, for example, a header of a bitstream.

As a method of encoding attribute information of a three-dimensionalpoint, it is conceivable to calculate a predicted value of the attributeinformation of the three-dimensional point and encode a difference(prediction residual) between the original value of the attributeinformation and the predicted value. For example, when the value ofattribute information at three-dimensional point p is Ap and a predictedvalue is Pp, the three-dimensional data encoding device encodesdifferential absolute value Diffp=|Ap−Pp|. In this case, whenhighly-accurate predicted value Pp can be generated, differentialabsolute value Diffp is small. Thus, for example, it is possible toreduce the code amount by entropy encoding differential absolute valueDiffp using a coding table that reduces an occurrence bit count morewhen differential absolute value Diffp is smaller.

As a method of generating a prediction value of attribute information,it is conceivable to use attribute information of a referencethree-dimensional point that is another three-dimensional point whichneighbors a current three-dimensional point to be encoded. Here, areference three-dimensional point is a three-dimensional point in arange of a predetermined distance from the current three-dimensionalpoint. For example, when there are current three-dimensional pointp=(x1, y1, z1) and three-dimensional point q=(x2, y2, z2), thethree-dimensional data encoding device calculates Euclidean distance d(p, q) between three-dimensional point p and three-dimensional point qrepresented by (Equation A1).

[Math. 1]

d(p,q)=√{square root over ((x1−y1)²+(x2−y2)²+(x3−y3)²)}  (Equation A1)

The three-dimensional data encoding device determines that the positionof three-dimensional point q is closer to the position of currentthree-dimensional point p when Euclidean distance d (p, q) is smallerthan predetermined threshold value THd, and determines to use the valueof the attribute information of three-dimensional point q to generate apredicted value of the attribute information of currentthree-dimensional point p. It is to be noted that the method ofcalculating the distance may be another method, and a Mahalanobisdistance or the like may be used. In addition, the three-dimensionaldata encoding device may determine not to use, in prediction processing,any three-dimensional point outside the predetermined range of distancefrom the current three-dimensional point. For example, whenthree-dimensional point r is present, and distance d (p, r) betweencurrent three-dimensional point p and three-dimensional point r islarger than or equal to threshold value THd, the three-dimensional dataencoding device may determine not to use three-dimensional point r forprediction. It is to be noted that the three-dimensional data encodingdevice may add the information indicating threshold value THd to, forexample, a header of a bitstream.

FIG. 33 is a diagram illustrating an example of three-dimensionalpoints. In this example, distance d (p, q) between currentthree-dimensional point p and three-dimensional point q is smaller thanthreshold value THd. Thus, the three-dimensional data encoding devicedetermines that three-dimensional point q is a referencethree-dimensional point of current three-dimensional point p, anddetermines to use the value of attribute information Aq ofthree-dimensional point q to generate predicted value Pp of attributeinformation Ap of current three-dimensional point p.

In contrast, distance d (p, r) between current three-dimensional point pand three-dimensional point r is larger than or equal to threshold valueTHd. Thus, the three-dimensional data encoding device determines thatthree-dimensional point r is not any reference three-dimensional pointof current three-dimensional point p, and determines not to use thevalue of attribute information Ar of three-dimensional point r togenerate predicted value Pp of attribute information Ap of currentthree-dimensional point p.

In addition, when encoding the attribute information of the currentthree-dimensional point using a predicted value, the three-dimensionaldata encoding device uses a three-dimensional point whose attributeinformation has already been encoded and decoded, as a referencethree-dimensional point. Likewise, when decoding the attributeinformation of a current three-dimensional point to be decoded, thethree-dimensional data decoding device uses a three-dimensional pointwhose attribute information has already been decoded, as a referencethree-dimensional point. In this way, it is possible to generate thesame predicted value at the time of encoding and decoding. Thus, abitstream of the three-dimensional point generated by the encoding canbe decoded correctly at the decoding side.

Furthermore, when encoding attribute information of each ofthree-dimensional points, it is conceivable to classify thethree-dimensional point into one of a plurality of layers using geometryinformation of the three-dimensional point and then encode the attributeinformation. Here, each of the layers classified is referred to as aLevel of Detail (LoD). A method of generating LoDs is explained withreference to FIG. 34 .

First, the three-dimensional data encoding device selects initial pointa0 and assigns initial point a0 to LoD0. Next, the three-dimensionaldata encoding device extracts point a1 distant from point a0 more thanthreshold value Thres_LoD[0] of LoD0 and assigns point a1 to LoD0. Next,the three-dimensional data encoding device extracts point a2 distantfrom point a1 more than threshold value Thres_LoD[0] of LoD0 and assignspoint a2 to LoD0. In this way, the three-dimensional data encodingdevice configures LoD0 in such a manner that the distance between thepoints in LoD0 is larger than threshold value Thres_LoD[0].

Next, the three-dimensional data encoding device selects point b0 whichhas not yet been assigned to any LoD and assigns point b0 to LoD1. Next,the three-dimensional data encoding device extracts point b1 which isdistant from point b0 more than threshold value Thres_LoD[1] of LoD1 andwhich has not yet been assigned to any LoD, and assigns point b1 toLoD1. Next, the three-dimensional data encoding device extracts point b2which is distant from point b1 more than threshold value Thres_LoD[1] ofLoD1 and which has not yet been assigned to any LoD, and assigns pointb2 to LoD1. In this way, the three-dimensional data encoding deviceconfigures LoD1 in such a manner that the distance between the points inLoD1 is larger than threshold value Thres_LoD[1].

Next, the three-dimensional data encoding device selects point c0 whichhas not yet been assigned to any LoD and assigns point c0 to LoD2. Next,the three-dimensional data encoding device extracts point c1 which isdistant from point c0 more than threshold value Thres_LoD[2] of LoD2 andwhich has not yet been assigned to any LoD, and assigns point c1 toLoD2. Next, the three-dimensional data encoding device extracts point c2which is distant from point c1 more than threshold value Thres_LoD[2] ofLoD2 and which has not yet been assigned to any LoD, and assigns pointc2 to LoD2. In this way, the three-dimensional data encoding deviceconfigures LoD2 in such a manner that the distance between the points inLoD2 is larger than threshold value Thres_LoD[2]. For example, asillustrated in FIG. 35 , threshold values Thres_LoD[0], Thres_LoD[1],and Thres_LoD[2] of respective LoDs are set.

In addition, the three-dimensional data encoding device may add theinformation indicating the threshold value of each LoD to, for example,a header of a bitstream. For example, in the case of the exampleillustrated in FIG. 35 , the three-dimensional data encoding device mayadd threshold values Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] ofrespective LoDs to a header.

Alternatively, the three-dimensional data encoding device may assign allthree-dimensional points which have not yet been assigned to any LoD inthe lowermost-layer LoD. In this case, the three-dimensional dataencoding device is capable of reducing the code amount of the header bynot assigning the threshold value of the lowermost-layer LoD to theheader. For example, in the case of the example illustrated in FIG. 35 ,the three-dimensional data encoding device assigns threshold valuesThres_LoD[0] and Thres_LoD[1] to the header, and does not assignThres_LoD[2] to the header. In this case, the three-dimensional dataencoding device may estimate value 0 of Thres_LoD[2]. In addition, thethree-dimensional data encoding device may add the number of LoDs to aheader. In this way, the three-dimensional data encoding device iscapable of determining the lowermost-layer LoD using the number of LoDs.

In addition, setting threshold values for the respective layers LoDs insuch a manner that a larger threshold value is set to a higher layermakes a higher layer (layer closer to LoD0) to have a sparse point cloud(sparse) in which three-dimensional points are more distant and makes alower layer to have a dense point cloud (dense) in whichthree-dimensional points are closer. It is to be noted that, in anexample illustrated in FIG. 35 , LoD0 is the uppermost layer.

In addition, the method of selecting an initial three-dimensional pointat the time of setting each LoD may depend on an encoding order at thetime of geometry information encoding. For example, thethree-dimensional data encoding device configures LoD0 by selecting thethree-dimensional point encoded first at the time of the geometryinformation encoding as initial point a0 of LoD0, and selecting point a1and point a2 from initial point a0 as the origin. The three-dimensionaldata encoding device then may select the three-dimensional point whosegeometry information has been encoded at the earliest time amongthree-dimensional points which do not belong to LoD0, as initial pointb0 of LoD1. In other words, the three-dimensional data encoding devicemay select the three-dimensional point whose geometry information hasbeen encoded at the earliest time among three-dimensional points whichdo not belong to layers (LoD0 to LoDn−1) above LoDn, as initial point n0of LoDn. In this way, the three-dimensional data encoding device iscapable of configuring the same LoD as in encoding by using, indecoding, the initial point selecting method similar to the one used inthe encoding, which enables appropriate decoding of a bitstream. Morespecifically, the three-dimensional data encoding device selects thethree-dimensional point whose geometry information has been decoded atthe earliest time among three-dimensional points which do not belong tolayers above LoDn, as initial point n0 of LoDn.

Hereinafter, a description is given of a method of generating thepredicted value of the attribute information of each three-dimensionalpoint using information of LoDs. For example, when encodingthree-dimensional points starting with the three-dimensional pointsincluded in LoD0, the three-dimensional data encoding device generatescurrent three-dimensional points which are included in LoD1 usingencoded and decoded (hereinafter also simply referred to as “encoded”)attribute information included in LoD0 and LoD1. In this way, thethree-dimensional data encoding device generates a predicted value ofattribute information of each three-dimensional point included in LoDnusing encoded attribute information included in LoDn′ (n′≤n). In otherwords, the three-dimensional data encoding device does not use attributeinformation of each of three-dimensional points included in any layerbelow LoDn to calculate a predicted value of attribute information ofeach of the three-dimensional points included in LoDn.

For example, the three-dimensional data encoding device calculates anaverage of attribute information of N or less three-dimensional pointsamong encoded three-dimensional points surrounding a currentthree-dimensional point to be encoded, to generate a predicted value ofattribute information of the current three-dimensional point. Inaddition, the three-dimensional data encoding device may add value N to,for example, a header of a bitstream. It is to be noted that thethree-dimensional data encoding device may change value N for eachthree-dimensional point, and may add value N for each three-dimensionalpoint. This enables selection of appropriate N for eachthree-dimensional point, which makes it possible to increase theaccuracy of the predicted value. Accordingly, it is possible to reducethe prediction residual. Alternatively, the three-dimensional dataencoding device may add value N to a header of a bitstream, and may fixthe value indicating N in the bitstream. This eliminates the need toencode or decode value N for each three-dimensional point, which makesit possible to reduce the processing amount. In addition, thethree-dimensional data encoding device may encode the values of Nseparately for each LoD. In this way, it is possible to increase thecoding efficiency by selecting appropriate N for each LoD.

Alternatively, the three-dimensional data encoding device may calculatea predicted value of attribute information of three-dimensional pointbased on weighted average values of attribute information of encoded Nneighbor three-dimensional points. For example, the three-dimensionaldata encoding device calculates weights using distance informationbetween a current three-dimensional point and each of N neighborthree-dimensional points.

When encoding value N for each LoD, for example, the three-dimensionaldata encoding device sets larger value N to a higher layer LoD, and setssmaller value N to a lower layer LoD. The distance betweenthree-dimensional points belonging to a higher layer LoD is large, thereis a possibility that it is possible to increase the prediction accuracyby setting large value N, selecting a plurality of neighborthree-dimensional points, and averaging the values. Furthermore, thedistance between three-dimensional points belonging to a lower layer LoDis small, it is possible to perform efficient prediction while reducingthe processing amount of averaging by setting smaller value N.

FIG. 36 is a diagram illustrating an example of attribute information tobe used for predicted values. As described above, the predicted value ofpoint P included in LoDN is generated using encoded neighbor point P′included in LoDN′ (N′≤N). Here, neighbor point P′ is selected based onthe distance from point P. For example, the predicted value of attributeinformation of point b2 illustrated in FIG. 36 is generated usingattribute information of each of points a0, a1, b0, and b1.

Neighbor points to be selected vary depending on the values of Ndescribed above. For example, in the case of N=5, a0, a1, a2, b0, and b1are selected as neighbor points. In the case of N=4, a0, a1, a2, and b1are selected based on distance information.

The predicted value is calculated by distance-dependent weightedaveraging. For example, in the example illustrated in FIG. 36 ,predicted value a2p of point a2 is calculated by weighted averaging ofattribute information of each of point a0 and a1, as represented by(Equation A2) and (Equation A3). It is to be noted that A_(I) is anattribute information value of ai.

[Math.2] $\begin{matrix}{{a2p} = {\sum\limits_{l = 0}^{1}{w_{i} \times A_{i}}}} & \left( {{Equation}{A2}} \right)\end{matrix}$ $\begin{matrix}{w_{i} = \frac{\frac{1}{d\left( {{a2},{ai}} \right)}}{\sum_{j = 0}^{1}\frac{1}{d\left( {{a2},{aj}} \right)}}} & \left( {{Equation}{A3}} \right)\end{matrix}$

In addition, predicted value b2p of point b2 is calculated by weightedaveraging of attribute information of each of point a0, a1, a2, b0, andb1, as represented by (Equation A4) and (Equation A6). It is to be notedthat B_(i) is an attribute information value of bi.

[Math.3] $\begin{matrix}{{b2p} = {{\sum_{i = 0}^{2}{{wa}_{i} \times A_{i}}} + {\sum_{i = 0}^{1}{{wb}_{i} \times B_{i}}}}} & \left( {{Equation}{A4}} \right)\end{matrix}$ $\begin{matrix}{{wa}_{i} = \frac{\frac{1}{d\left( {{b2},{ai}} \right)}}{{\sum_{j = 0}^{2}\frac{1}{d\left( {{b2},{aj}} \right)}} + {\sum_{j = 0}^{1}\frac{1}{d\left( {{b2},{bj}} \right)}}}} & \left( {{Equation}{A5}} \right)\end{matrix}$ $\begin{matrix}{{wb}_{i} = \frac{\frac{1}{d\left( {{b2},{bi}} \right)}}{{\sum_{j = 0}^{2}\frac{1}{d\left( {{b2},{aj}} \right)}} + {\sum_{j = 0}^{1}\frac{1}{d\left( {{b2},{bj}} \right)}}}} & \left( {{Equation}{A6}} \right)\end{matrix}$

In addition, the three-dimensional data encoding device may calculate adifference value (prediction residual) generated from the value ofattribute information of a three-dimensional point and neighbor points,and may quantize the calculated prediction residual. For example, thethree-dimensional data encoding device performs quantization by dividingthe prediction residual by a quantization scale (also referred to as aquantization step). In this case, an error (quantization error) whichmay be generated by quantization reduces as the quantization scale issmaller. In the other case where the quantization scale is larger, theresulting quantization error is larger.

It is to be noted that the three-dimensional data encoding device maychange the quantization scale to be used for each LoD. For example, thethree-dimensional data encoding device reduces the quantization scalemore for a higher layer, and increases the quantization scale more for alower layer. The value of attribute information of a three-dimensionalpoint belonging to a higher layer may be used as a predicted value ofattribute information of a three-dimensional point belonging to a lowerlayer. Thus, it is possible to increase the coding efficiency byreducing the quantization scale for the higher layer to reduce thequantization error that can be generated in the higher layer and toincrease the prediction accuracy of the predicted value. It is to benoted that the three-dimensional data encoding device may add thequantization scale to be used for each LoD to, for example, a header. Inthis way, the three-dimensional data encoding device can decode thequantization scale correctly, thereby appropriately decoding thebitstream.

In addition, the three-dimensional data encoding device may convert asigned integer value (signed quantized value) which is a quantizedprediction residual into an unsigned integer value (unsigned quantizedvalue). This eliminates the need to consider occurrence of a negativeinteger when entropy encoding the prediction residual. It is to be notedthat the three-dimensional data encoding device does not always need toconvert a signed integer value into an unsigned integer value, and, forexample, that the three-dimensional data encoding device may entropyencode a sign bit separately.

The prediction residual is calculated by subtracting a prediction valuefrom the original value. For example, as represented by (Equation A7),prediction residual a2r of point a2 is calculated by subtractingpredicted value a2p of point a2 from value A₂ of attribute informationof point a2. As represented by (Equation A8), prediction residual b2r ofpoint b2 is calculated by subtracting predicted value b2p of point b2from value B₂ of attribute information of point b2.

a2r=A ₂ −a2p  (Equation A7)

b2r=B ₂ −b2p  (Equation A8)

In addition, the prediction residual is quantized by being divided by aQuantization Step (QS). For example, quantized value a2q of point a2 iscalculated according to (Equation A9). Quantized value b2q of point b2is calculated according to (Equation A10). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2q=a2r/QS_LoD0  (Equation A9)

b2q=b2r/QS_LoD1  (Equation A10)

In addition, the three-dimensional data encoding device converts signedinteger values which are quantized values as indicated below intounsigned integer values as indicated below. When signed integer valuea2q is smaller than 0, the three-dimensional data encoding device setsunsigned integer value a2u to −1−(2×a2q). When signed integer value a2qis 0 or more, the three-dimensional data encoding device sets unsignedinteger value a2u to 2×a2q.

Likewise, when signed integer value b2q is smaller than 0, thethree-dimensional data encoding device sets unsigned integer value b2uto −1−(2×b2q). When signed integer value b2q is 0 or more, thethree-dimensional data encoding device sets unsigned integer value b2uto 2×b2q.

In addition, the three-dimensional data encoding device may encode thequantized prediction residual (unsigned integer value) by entropyencoding. For example, the three-dimensional data encoding device maybinarize the unsigned integer value and then apply binary arithmeticencoding to the binary value.

It is to be noted that, in this case, the three-dimensional dataencoding device may switch binarization methods according to the valueof a prediction residual. For example, when prediction residual pu issmaller than threshold value R_TH, the three-dimensional data encodingdevice binarizes prediction residual pu using a fixed bit count requiredfor representing threshold value R_TH. In addition, when predictionresidual pu is larger than or equal to threshold value R_TH, thethree-dimensional data encoding device binarizes the binary data ofthreshold value R_TH and the value of (pu−R_TH), usingexponential-Golomb coding, or the like.

For example, when threshold value R_TH is 63 and prediction residual puis smaller than 63, the three-dimensional data encoding device binarizesprediction residual pu using 6 bits. When prediction residual pu islarger than or equal to 63, the three-dimensional data encoding deviceperforms arithmetic encoding by binarizing the binary data (111111) ofthreshold value R_TH and (pu−63) using exponential-Golomb coding.

In a more specific example, when prediction residual pu is 32, thethree-dimensional data encoding device generates 6-bit binary data(100000), and arithmetic encodes the bit sequence. In addition, whenprediction residual pu is 66, the three-dimensional data encoding devicegenerates binary data (111111) of threshold value R_TH and a bitsequence (00100) representing value 3 (66-63) using exponential-Golombcoding, and arithmetic encodes the bit sequence (111111+00100).

In this way, the three-dimensional data encoding device can performencoding while preventing a binary bit count from increasing abruptly inthe case where a prediction residual becomes large by switchingbinarization methods according to the magnitude of the predictionresidual. It is to be noted that the three-dimensional data encodingdevice may add threshold value R_TH to, for example, a header of abitstream.

For example, in the case where encoding is performed at a high bit rate,that is, when a quantization scale is small, a small quantization errorand a high prediction accuracy are obtained. As a result, a predictionresidual may not be large. Thus, in this case, the three-dimensionaldata encoding device sets large threshold value R_TH. This reduces thepossibility that the binary data of threshold value R_TH is encoded,which increases the coding efficiency. In the opposite case whereencoding is performed at a low bit rate, that is, when a quantizationscale is large, a large quantization error and a low prediction accuracyare obtained. As a result, a prediction residual may be large. Thus, inthis case, the three-dimensional data encoding device sets smallthreshold value R_TH. In this way, it is possible to prevent abruptincrease in bit length of binary data.

In addition, the three-dimensional data encoding device may switchthreshold value R_TH for each LoD, and may add threshold value R_TH foreach LoD to, for example, a header. In other words, thethree-dimensional data encoding device may switch binarization methodsfor each LoD. For example, since distances between three-dimensionalpoints are large in a higher layer, a prediction accuracy is low, whichmay increase a prediction residual. Thus, the three-dimensional dataencoding device prevents abrupt increase in bit length of binary data bysetting small threshold value R_TH to the higher layer. In addition,since distances between three-dimensional points are small in a lowerlayer, a prediction accuracy is high, which may reduce a predictionresidual. Thus, the three-dimensional data encoding device increases thecoding efficiency by setting large threshold value R_TH to the lowerlayer.

FIG. 37 is a diagram indicating examples of exponential-Golomb codes.The diagram indicates the relationships between pre-binarization values(non-binary values) and post-binarization bits (codes). It is to benoted that 0 and 1 indicated in FIG. 37 may be inverted.

The three-dimensional data encoding device applies arithmetic encodingto the binary data of prediction residuals. In this way, the codingefficiency can be increased. It is to be noted that, in the applicationof the arithmetic encoding, there is a possibility that occurrenceprobability tendencies of 0 and 1 in each bit vary, in binary data,between an n-bit code which is a part binarized by n bits and aremaining code which is a part binarized using exponential-Golombcoding. Thus, the three-dimensional data encoding device may switchmethods of applying arithmetic encoding between the n-bit code and theremaining code.

For example, the three-dimensional data encoding device performsarithmetic encoding on the n-bit code using one or more coding tables(probability tables) different for each bit. At this time, thethree-dimensional data encoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional dataencoding device performs arithmetic encoding using one coding table forfirst bit b0 in an n-bit code. The three-dimensional data encodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata encoding device switches coding tables to be used for arithmeticencoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data encoding device uses four coding tables for thenext bit b2. The three-dimensional data encoding device switches codingtables to be used for arithmetic encoding of bit b2 according to thevalues (in a range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data encoding device uses 2^(n-1)coding tables when arithmetic encoding each bit bn−1 in n-bit code. Thethree-dimensional data encoding device switches coding tables to be usedaccording to the values (occurrence patterns) of bits before bn−1. Inthis way, the three-dimensional data encoding device can use codingtables appropriate for each bit, and thus can increase the codingefficiency.

It is to be noted that the three-dimensional data encoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data encoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic encoding each bit bn−1. In this way, it ispossible to increase the coding efficiency while reducing the number ofcoding tables to be used for each bit. It is to be noted that thethree-dimensional data encoding device may update the occurrenceprobabilities of 0 and 1 in each coding table according to the values ofbinary data occurred actually. In addition, the three-dimensional dataencoding device may fix the occurrence probabilities of 0 and 1 incoding tables for some bit(s). In this way, it is possible to reduce thenumber of updates of occurrence probabilities, and thus to reduce theprocessing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one table (CTb0). Coding tables for b1 are two tables(CTb10 and CTb11). Coding tables to be used are switched according tothe value (0 or 1) of b0. Coding tables for b2 are four tables (CTb20,CTb21, CTb22, and CTb23). Coding tables to be used are switchedaccording to the values (in the range from 0 to 3) of b0 and b1. Codingtables for bn−1 are 2^(n-1) tables (CTbn0, CTbn1, . . . , CTbn(2^(n-1)−1)). Coding tables to be used are switched according to thevalues (in a range from 0 to 2^(n-1)−1) of b0, b1, . . . , bn−2.

It is to be noted that the three-dimensional data encoding device mayapply, to an n-bit code, arithmetic encoding (m=2^(n)) by m-ary thatsets the value in the range from 0 to 2^(n)−1 without binarization. Whenthe three-dimensional data encoding device arithmetic encodes an n-bitcode by an m-ary, the three-dimensional data decoding device mayreconstruct the n-bit code by arithmetic decoding the m-ary.

FIG. 38 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 38 ,each remaining code which is a part binarized using exponential-Golombcoding includes a prefix and a suffix. For example, thethree-dimensional data encoding device switches coding tables betweenthe prefix and the suffix. In other words, the three-dimensional dataencoding device arithmetic encodes each of bits included in the prefixusing coding tables for the prefix, and arithmetic encodes each of bitsincluded in the suffix using coding tables for the suffix.

It is to be noted that the three-dimensional data encoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred actually. In addition,the three-dimensional data encoding device may fix the occurrenceprobabilities of 0 and 1 in one of coding tables. In this way, it ispossible to reduce the number of updates of occurrence probabilities,and thus to reduce the processing amount. For example, thethree-dimensional data encoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

In addition, the three-dimensional data encoding device decodes aquantized prediction residual by inverse quantization andreconstruction, and uses a decoded value which is the decoded predictionresidual for prediction of a current three-dimensional point to beencoded and the following three-dimensional point(s). More specifically,the three-dimensional data encoding device calculates an inversequantized value by multiplying the quantized prediction residual(quantized value) with a quantization scale, and adds the inversequantized value and a prediction value to obtain the decoded value(reconstructed value).

For example, quantized value a2iq of point a2 is calculated usingquantized value a2q of point a2 according to (Equation A11). Inversequantized value b2iq of point b2q is calculated using quantized valueb2q of point b2 according to (Equation A12). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2iq=a2q×QS_LoD0  (Equation A11)

b2iq=b2q×QS_LoD1  (Equation A12)

For example, as represented by (Equation A13), decoded value a2rec ofpoint a2 is calculated by adding inverse quantization value a2iq ofpoint a2 to predicted value a2p of point a2. As represented by (EquationA14), decoded value b2rec of point b2 is calculated by adding inversequantized value b2iq of point b2 to predicted value b2p of point b2.

a2rec=a2iq+a2p  (Equation A13)

b2rec=b2iq+b2p  (Equation A14)

Hereinafter, a syntax example of a bitstream according to the presentembodiment is described. FIG. 39 is a diagram indicating the syntaxexample of an attribute header (attribute_header) according to thepresent embodiment. The attribute header is header information ofattribute information. As indicated in FIG. 39 , the attribute headerincludes the number of layers information (NumLoD), the number ofthree-dimensional points information (NumOfPoint[i]), a layer thresholdvalue (Thres_LoD[i]), the number of neighbor points information(NumNeighborPoint[i]), a prediction threshold value (THd[i]), aquantization scale (QS[i]), and a binarization threshold value(R_TH[i]).

The number of layers information (NumLoD) indicates the number of LoDsto be used.

The number of three-dimensional points information (NumOfPoint[i])indicates the number of three-dimensional points belonging to layer i.It is to be noted that the three-dimensional data encoding device mayadd, to another header, the number of three-dimensional pointsinformation indicating the total number of three-dimensional points. Inthis case, the three-dimensional data encoding device does not need toadd, to a header, NumOfPoint [NumLoD−1] indicating the number ofthree-dimensional points belonging to the lowermost layer. In this case,the three-dimensional data decoding device is capable of calculatingNumOfPoint [NumLoD−1] according to (Equation A15). In this case, it ispossible to reduce the code amount of the header.

[Math.4] $\begin{matrix}{{{{Num}{OfPoint}}\left\lbrack {{NumLoD} - 1} \right\rbrack} = {{{All}{Num}{OfPoint}} - {\sum\limits_{j = 0}^{NumLoD}{{{Num}{OfPoint}}\lbrack f\rbrack}}}} & \left( {{Equation}{A15}} \right)\end{matrix}$

The layer threshold value (Thres_LoD[i]) is a threshold value to be usedto set layer i. The three-dimensional data encoding device and thethree-dimensional data decoding device configure LoDi in such a mannerthat the distance between points in LoDi becomes larger than thresholdvalue Thres_LoD[i]. The three-dimensional data encoding device does notneed to add the value of Thres_LoD [NumLoD−1] (lowermost layer) to aheader. In this case, the three-dimensional data decoding device mayestimate 0 as the value of Thres_LoD [NumLoD−1]. In this case, it ispossible to reduce the code amount of the header.

The number of neighbor points information (NumNeighborPoint[i])indicates the upper limit value of the number of neighbor points to beused to generate a predicted value of a three-dimensional pointbelonging to layer i. The three-dimensional data encoding device maycalculate a predicted value using the number of neighbor points M whenthe number of neighbor points M is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]). Furthermore, when there is no need todifferentiate the values of NumNeighborPoint[i] for respective LoDs, thethree-dimensional data encoding device may add a piece of the number ofneighbor points information (NumNeighborPoint) to be used in all LoDs toa header.

The prediction threshold value (THd[i]) indicates the upper limit valueof the distance between a current three-dimensional point to be encodedor decoded in layer i and each of neighbor three-dimensional points tobe used to predict the current three-dimensional point. Thethree-dimensional data encoding device and the three-dimensional datadecoding device do not use, for prediction, any three-dimensional pointdistant from the current three-dimensional point over THd[i]. It is tobe noted that, when there is no need to differentiate the values ofTHd[i] for respective LoDs, the three-dimensional data encoding devicemay add a single prediction threshold value (THd) to be used in all LoDsto a header.

The quantization scale (QS[i]) indicates a quantization scale to be usedfor quantization and inverse quantization in layer i.

The binarization threshold value (R_TH[i]) is a threshold value forswitching binarization methods of prediction residuals ofthree-dimensional points belonging to layer i. For example, thethree-dimensional data encoding device binarizes prediction residual puusing a fixed bit count when a prediction residual is smaller thanthreshold value R_TH, and binarizes the binary data of threshold valueR_TH and the value of (pu−R_TH) using exponential-Golomb coding when aprediction residual is larger than or equal to threshold value R_TH. Itis to be noted that, when there is no need to switch the values ofR_TH[i] between LoDs, the three-dimensional data encoding device may adda single binarization threshold value (R_TH) to be used in all LoDs to aheader.

It is to be noted that R_TH[i] may be the maximum value which can berepresented by n bits. For example, R_TH is 63 in the case of 6 bits,and R_TH is 255 in the case of 8 bits. Alternatively, thethree-dimensional data encoding device may encode a bit count instead ofencoding the maximum value which can be represented by n bits as abinarization threshold value. For example, the three-dimensional dataencoding device may add value 6 in the case of R_TH[i]=63 to a header,and may add value 8 in the case of R_TH[i]=255 to a header.Alternatively, the three-dimensional data encoding device may define theminimum value (minimum bit count) representing R_TH[i], and add arelative bit count from the minimum value to a header. For example, thethree-dimensional data encoding device may add value 0 to a header whenR_TH[i]=63 is satisfied and the minimum bit count is 6, and may addvalue 2 to a header when R_TH[i]=255 is satisfied and the minimum bitcount is 6.

Alternatively, the three-dimensional data encoding device may entropyencode at least one of NumLoD, Thres_LoD[i], NumNeighborPoint[i],THd[i], QS[i], and R_TH[i], and add the entropy encoded one to a header.For example, the three-dimensional data encoding device may binarizeeach value and perform arithmetic encoding on the binary value. Inaddition, the three-dimensional data encoding device may encode eachvalue using a fixed length in order to reduce the processing amount.

Alternatively, the three-dimensional data encoding device does notalways need to add at least one of NumLoD, Thres_LoD[i],NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to a header. Forexample, at least one of these values may be defined by a profile or alevel in a standard, or the like. In this way, it is possible to reducethe bit amount of the header.

FIG. 40 is a diagram indicating the syntax example of attribute data(attribute_data) according to the present embodiment. The attribute dataincludes encoded data of the attribute information of a plurality ofthree-dimensional points. As indicated in FIG. 40 , the attribute dataincludes an n-bit code and a remaining code.

The n-bit code is encoded data of a prediction residual of a value ofattribute information or a part of the encoded data. The bit length ofthe n-bit code depends on value R_TH[i]. For example, the bit length ofthe n-bit code is 6 bits when the value indicated by R_TH[i] is 63, thebit length of the n-bit code is 8 bits when the value indicated byR_TH[i] is 255.

The remaining code is encoded data encoded using exponential-Golombcoding among encoded data of the prediction residual of the value of theattribute information. The remaining code is encoded or decoded when thevalue of the n-bit code is equal to R_TH[i]. The three-dimensional datadecoding device decodes the prediction residual by adding the value ofthe n-bit code and the value of the remaining code. It is to be notedthat the remaining code does not always need to be encoded or decodedwhen the value of the n-bit code is not equal to R_TH[i].

Hereinafter, a description is given of a flow of processing in thethree-dimensional data encoding device. FIG. 41 is a flowchart of athree-dimensional data encoding process performed by thethree-dimensional data encoding device.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3001). For example, the three-dimensional dataencoding is performed using octree representation.

When the positions of three-dimensional points changed by quantization,etc, after the encoding of the geometry information, thethree-dimensional data encoding device re-assigns attribute informationof the original three-dimensional points to the post-changethree-dimensional points (S3002). For example, the three-dimensionaldata encoding device interpolates values of attribute informationaccording to the amounts of change in position to re-assign theattribute information. For example, the three-dimensional data encodingdevice detects pre-change N three-dimensional points closer to thepost-change three-dimensional positions, and performs weighted averagingof the values of attribute information of the N three-dimensionalpoints. For example, the three-dimensional data encoding devicedetermines weights based on distances from the post-changethree-dimensional positions to the respective N three-dimensionalpositions in weighted averaging. The three-dimensional data encodingdevice then determines the values obtained through the weightedaveraging to be the values of the attribute information of thepost-change three-dimensional points. When two or more of thethree-dimensional points are changed to the same three-dimensionalposition through quantization, etc., the three-dimensional data encodingdevice may assign the average value of the attribute information of thepre-change two or more three-dimensional points as the values of theattribute information of the post-change three-dimensional points.

Next, the three-dimensional data encoding device encodes the attributeinformation (attribute) re-assigned (S3003). For example, when encodinga plurality of kinds of attribute information, the three-dimensionaldata encoding device may encode the plurality of kinds of attributeinformation in order. For example, when encoding colors and reflectancesas attribute information, the three-dimensional data encoding device maygenerate a bitstream added with the color encoding results and thereflectance encoding results after the color encoding results. It is tobe noted that the order of the plurality of encoding results ofattribute information to be added to a bitstream is not limited to theorder, and may be any order.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device.Alternatively, the three-dimensional data encoding device may encode aplurality of kinds of attribute information in parallel, and mayintegrate the encoding results into a single bitstream. In this way, thethree-dimensional data encoding device is capable of encoding theplurality of kinds of attribute information at high speed.

FIG. 42 is a flowchart of an attribute information encoding process(S3003). First, the three-dimensional data encoding device sets LoDs(S3011). In other words, the three-dimensional data encoding deviceassigns each of three-dimensional points to any one of the plurality ofLoDs.

Next, the three-dimensional data encoding device starts a loop for eachLoD (S3012). In other words, the three-dimensional data encoding deviceiteratively performs the processes of Steps from S3013 to S3021 for eachLoD.

Next, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3013). In other words, the three-dimensionaldata encoding device iteratively performs the processes of Steps fromS3014 to S3020 for each three-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point (S3014). Next, the three-dimensional dataencoding device calculates the weighted average of the values ofattribute information of the plurality of neighbor points, and sets theresulting value to predicted value P (S3015). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of the currentthree-dimensional point and the predicted value (S3016). Next, thethree-dimensional data encoding device quantizes the prediction residualto calculate a quantized value (S3017). Next, the three-dimensional dataencoding device arithmetic encodes the quantized value (S3018).

Next, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3019). Next,the three-dimensional data encoding device adds a prediction value tothe inverse quantized value to generate a decoded value (S3020). Next,the three-dimensional data encoding device ends the loop for eachthree-dimensional point (S3021). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3022).

Hereinafter, a description is given of a three-dimensional data decodingprocess in the three-dimensional data decoding device which decodes abitstream generated by the three-dimensional data encoding device.

The three-dimensional data decoding device generates decoded binary databy arithmetic decoding the binary data of the attribute information inthe bitstream generated by the three-dimensional data encoding device,according to the method similar to the one performed by thethree-dimensional data encoding device. It is to be noted that whenmethods of applying arithmetic encoding are switched between the part(n-bit code) binarized using n bits and the part (remaining code)binarized using exponential-Golomb coding in the three-dimensional dataencoding device, the three-dimensional data decoding device performsdecoding in conformity with the arithmetic encoding, when applyingarithmetic decoding.

For example, the three-dimensional data decoding device performsarithmetic decoding using coding tables (decoding tables) different foreach bit in the arithmetic decoding of the n-bit code. At this time, thethree-dimensional data decoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional datadecoding device performs arithmetic decoding using one coding table forfirst bit b0 in the n-bit code. The three-dimensional data decodingdevice uses two coding tables for the next bit b1. The three-dimensionaldata decoding device switches coding tables to be used for arithmeticdecoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data decoding device uses four coding tables for thenext bit b2. The three-dimensional data decoding device switches codingtables to be used for arithmetic decoding of bit b2 according to thevalues (in the range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data decoding device uses 2^(n-1)coding tables when arithmetic decoding each bit bn−1 in the n-bit code.The three-dimensional data decoding device switches coding tables to beused according to the values (occurrence patterns) of bits before bn−1.In this way, the three-dimensional data decoding device is capable ofappropriately decoding a bitstream encoded at an increased codingefficiency using the coding tables appropriate for each bit.

It is to be noted that the three-dimensional data decoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data decoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic decoding each bit bn−1. In this way, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at the increased coding efficiency whilereducing the number of coding tables to be used for each bit. It is tobe noted that the three-dimensional data decoding device may update theoccurrence probabilities of 0 and 1 in each coding table according tothe values of binary data occurred actually. In addition, thethree-dimensional data decoding device may fix the occurrenceprobabilities of 0 and 1 in coding tables for some bit(s). In this way,it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one (CTb0). Coding tables for b1 are two tables (CTb10and CTb11). Coding tables to be used are switched according to the value(0 or 1) of b0. Coding tables for b2 are four tables (CTb20, CTb21,CTb22, and CTb23). Coding tables to be used according to the values (inthe range from 0 to 3) of b0 and b1. Coding tables for bn−1 are 2^(n-1)tables (CTbn0, CTbn1, . . . , CTbn (2^(n-1)−1)). Coding tables to beused are switched according to the values (in the range from 0 to2^(n-1)−1) of b0, b1, . . . , bn−2.

FIG. 43 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 43 ,the part (remaining part) binarized and encoded by the three-dimensionaldata encoding device using exponential-Golomb coding includes a prefixand a suffix. For example, the three-dimensional data decoding deviceswitches coding tables between the prefix and the suffix. In otherwords, the three-dimensional data decoding device arithmetic decodeseach of bits included in the prefix using coding tables for the prefix,and arithmetic decodes each of bits included in the suffix using codingtables for the suffix.

It is to be noted that the three-dimensional data decoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred at the time of decoding.In addition, the three-dimensional data decoding device may fix theoccurrence probabilities of 0 and 1 in one of coding tables. In thisway, it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount. For example,the three-dimensional data decoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

Furthermore, the three-dimensional data decoding device decodes thequantized prediction residual (unsigned integer value) by debinarizingthe binary data of the prediction residual arithmetic decoded accordingto a method in conformity with the encoding method used by thethree-dimensional data encoding device. The three-dimensional datadecoding device first arithmetic decodes the binary data of an n-bitcode to calculate a value of the n-bit code. Next, the three-dimensionaldata decoding device compares the value of the n-bit code with thresholdvalue R_TH.

In the case where the value of the n-bit code and threshold value R_THmatch, the three-dimensional data decoding device determines that a bitencoded using exponential-Golomb coding is present next, and arithmeticdecodes the remaining code which is the binary data encoded usingexponential-Golomb coding. The three-dimensional data decoding devicethen calculates, from the decoded remaining code, a value of theremaining code using a reverse lookup table indicating the relationshipbetween the remaining code and the value. FIG. 44 is a diagramindicating an example of a reverse lookup table indicating relationshipsbetween remaining codes and the values thereof. Next, thethree-dimensional data decoding device adds the obtained value of theremaining code to R_TH, thereby obtaining a debinarized quantizedprediction residual.

In the opposite case where the value of the n-bit code and thresholdvalue R_TH do not match (the value of the n-bit code is smaller thanvalue R_TH), the three-dimensional data decoding device determines thevalue of the n-bit code to be the debinarized quantized predictionresidual as it is. In this way, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream generatedwhile switching the binarization methods according to the values of theprediction residuals by the three-dimensional data encoding device.

It is to be noted that, when threshold value R_TH is added to, forexample, a header of a bitstream, the three-dimensional data decodingdevice may decode threshold value R_TH from the header, and may switchdecoding methods using decoded threshold value R_TH. When thresholdvalue R_TH is added to, for example, a header for each LoD, thethree-dimensional data decoding device switch decoding methods usingthreshold value R_TH decoded for each LoD.

For example, when threshold value R_TH is 63 and the value of thedecoded n-bit code is 63, the three-dimensional data decoding devicedecodes the remaining code using exponential-Golomb coding, therebyobtaining the value of the remaining code. For example, in the exampleindicated in FIG. 44 , the remaining code is 00100, and 3 is obtained asthe value of the remaining code. Next, the three-dimensional datadecoding device adds 63 that is threshold value R_TH and 3 that is thevalue of the remaining code, thereby obtaining 66 that is the value ofthe prediction residual.

In addition, when the value of the decoded n-bit code is 32, thethree-dimensional data decoding device sets 32 that is the value of then-bit code to the value of the prediction residual.

In addition, the three-dimensional data decoding device converts thedecoded quantized prediction residual, for example, from an unsignedinteger value to a signed integer value, through processing inverse tothe processing in the three-dimensional data encoding device. In thisway, when entropy decoding the prediction residual, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream generated without considering occurrence of anegative integer. It is to be noted that the three-dimensional datadecoding device does not always need to convert an unsigned integervalue to a signed integer value, and that, for example, thethree-dimensional data decoding device may decode a sign bit whendecoding a bitstream generated by separately entropy encoding the signbit.

The three-dimensional data decoding device performs decoding by inversequantizing and reconstructing the quantized prediction residual afterbeing converted to the signed integer value, to obtain a decoded value.The three-dimensional data decoding device uses the generated decodedvalue for prediction of a current three-dimensional point to be decodedand the following three-dimensional point(s). More specifically, thethree-dimensional data decoding device multiplies the quantizedprediction residual by a decoded quantization scale to calculate aninverse quantized value and adds the inverse quantized value and thepredicted value to obtain the decoded value.

The decoded unsigned integer value (unsigned quantized value) isconverted into a signed integer value through the processing indicatedbelow. When the least significant bit (LSB) of decoded unsigned integervalue a2u is 1, the three-dimensional data decoding device sets signedinteger value a2q to −((a2u+1)>>1). When the LSB of unsigned integervalue a2u is not 1, the three-dimensional data decoding device setssigned integer value a2q to ((a2u>>1).

Likewise, when an LSB of decoded unsigned integer value b2u is 1, thethree-dimensional data decoding device sets signed integer value b2q to−((b2u+1)>>1). When the LSB of decoded unsigned integer value n2u is not1, the three-dimensional data decoding device sets signed integer valueb2q to ((b2u>>1).

Details of the inverse quantization and reconstruction processing by thethree-dimensional data decoding device are similar to the inversequantization and reconstruction processing in the three-dimensional dataencoding device.

Hereinafter, a description is given of a flow of processing in thethree-dimensional data decoding device. FIG. 45 is a flowchart of athree-dimensional data decoding process performed by thethree-dimensional data decoding device. First, the three-dimensionaldata decoding device decodes geometry information (geometry) from abitstream (S3031). For example, the three-dimensional data decodingdevice performs decoding using octree representation.

Next, the three-dimensional data decoding device decodes attributeinformation (attribute) from the bitstream (S3032). For example, whendecoding a plurality of kinds of attribute information, thethree-dimensional data decoding device may decode the plurality of kindsof attribute information in order. For example, when decoding colors andreflectances as attribute information, the three-dimensional datadecoding device decodes the color encoding results and the reflectanceencoding results in order of assignment in the bitstream. For example,when the reflectance encoding results are added after the color encodingresults in a bitstream, the three-dimensional data decoding devicedecodes the color encoding results, and then decodes the reflectanceencoding results. It is to be noted that the three-dimensional datadecoding device may decode, in any order, the encoding results of theattribute information added to the bitstream.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device. Inaddition, the three-dimensional data decoding device may decode aplurality of kinds of attribute information in parallel, and mayintegrate the decoding results into a single three-dimensional pointcloud. In this way, the three-dimensional data decoding device iscapable of decoding the plurality of kinds of attribute information athigh speed.

FIG. 46 is a flowchart of an attribute information decoding process(S3032). First, the three-dimensional data decoding device sets LoDs(S3041). In other words, the three-dimensional data decoding deviceassigns each of three-dimensional points having the decoded geometryinformation to any one of the plurality of LoDs. For example, thisassignment method is the same as the assignment method used in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device starts a loop for eachLoD (S3042). In other words, the three-dimensional data decoding deviceiteratively performs the processes of Steps from S3043 to S3049 for eachLoD.

Next, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3043). In other words, the three-dimensionaldata decoding device iteratively performs the processes of Steps fromS3044 to S3048 for each three-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point to be processed (S3044). Next, thethree-dimensional data decoding device calculates the weighted averageof the values of attribute information of the plurality of neighborpoints, and sets the resulting value to predicted value P (S3045). It isto be noted that these processes are similar to the processes in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device arithmetic decodes thequantized value from the bitstream (S3046). The three-dimensional datadecoding device inverse quantizes the decoded quantized value tocalculate an inverse quantized value (S3047). Next, thethree-dimensional data decoding device adds a predicted value to theinverse quantized value to generate a decoded value (S3048). Next, thethree-dimensional data decoding device ends the loop for eachthree-dimensional point (S3049). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3050).

The following describes configurations of the three-dimensional dataencoding device and three-dimensional data decoding device according tothe present embodiment. FIG. 47 is a block diagram illustrating aconfiguration of three-dimensional data encoding device 3000 accordingto the present embodiment. Three-dimensional data encoding device 3000includes geometry information encoder 3001, attribute informationre-assigner 3002, and attribute information encoder 3003.

Attribute information encoder 3003 encodes geometry information(geometry) of a plurality of three-dimensional points included in aninput point cloud. Attribute information re-assigner 3002 re-assigns thevalues of attribute information of the plurality of three-dimensionalpoints included in the input point cloud, using the encoding anddecoding results of the geometry information. Attribute informationencoder 3003 encodes the re-assigned attribute information (attribute).Furthermore, three-dimensional data encoding device 3000 generates abitstream including the encoded geometry information and the encodedattribute information.

FIG. 48 is a block diagram illustrating a configuration ofthree-dimensional data decoding device 3010 according to the presentembodiment. Three-dimensional data decoding device 3010 includesgeometry information decoder 3011 and attribute information decoder3012.

Geometry information decoder 3011 decodes the geometry information(geometry) of a plurality of three-dimensional points from a bitstream.Attribute information decoder 3012 decodes the attribute information(attribute) of the plurality of three-dimensional points from thebitstream. Furthermore, three-dimensional data decoding device 3010integrates the decoded geometry information and the decoded attributeinformation to generate an output point cloud.

Embodiment 4

Hereinafter, a three-dimensional point to be encoded is referred to as afirst three-dimensional point, and one or more three-dimensional pointsin the vicinity of the first three-dimensional point is referred to asone or more second three-dimensional points in some cases.

For example, in generating of a predicted value of an attributeinformation item (attribute information) of a three-dimensional point,an attribute value as it is of a closest three-dimensional point amongencoded and decoded three-dimensional points in the vicinity of athree-dimensional point to be encoded may be generated as a predictedvalue. In the generating of the predicted value, prediction modeinformation (PredMode) may be appended for each three-dimensional point,and one predicted value may be selected from a plurality of predictedvalues to allow generation of a predicted value. Specifically, forexample, it is conceivable that, for total number M of prediction modes,an average value is assigned to prediction mode 0, an attribute value ofthree-dimensional point A is assigned to prediction mode 1, . . . , andan attribute value of three-dimensional point Z is assigned toprediction mode M−1, and the prediction mode used for prediction isappended to a bitstream for each three-dimensional point. As such, afirst prediction mode value indicating a first prediction mode forcalculating, as a predicted value, an average of attribute informationitems of the surrounding three-dimensional points may be smaller than asecond prediction mode value indicating a second prediction mode forcalculating, as a predicted value, an attribute information item as itis of a surrounding three-dimensional point. Here, the “average value”as the predicted value calculated in prediction mode 0 is an averagevalue of the attribute values of the three-dimensional points in thevicinity of the three-dimensional point to be encoded.

FIG. 49 is a diagram showing a first example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. FIG. 50 is a diagram showing examples of attributeinformation items used as the predicted values according to Embodiment4. FIG. 51 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4.

Number M of prediction modes may be appended to a bitstream. Number M ofprediction modes may be defined by a profile or a level of standardsrather than appended to the bitstream. Number M of prediction modes maybe also calculated from number N of three-dimensional points used forprediction. For example, number M of prediction modes may be calculatedby M=N+1.

The table in FIG. 49 is an example of a case with number N ofthree-dimensional points used for prediction being 4 and number M ofprediction modes being 5. A predicted value of an attribute informationitem of point b2 can be generated by using attribute information itemsof points a0, a1, a2, b1. In selecting one prediction mode from aplurality of prediction modes, a prediction mode for generating, as apredicted value, an attribute value of each of points a0, a1, a2, b1 maybe selected in accordance with distance information from point b2 toeach of points a0, a1, a2, b1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

The table in FIG. 51 is, as in FIG. 49 , an example of a case withnumber N of three-dimensional points used for prediction being 4 andnumber M of prediction modes being 5. A predicted value of an attributeinformation item of point a2 can be generated by using attributeinformation items of points a0, a1. In selecting one prediction modefrom a plurality of prediction modes, a prediction mode for generating,as a predicted value, an attribute value of each of points a0 and a1 maybe selected in accordance with distance information from point a2 toeach of points a0, a1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

When the number of neighboring points, that is, number N of surroundingthree-dimensional points is smaller than four such as at point a2 above,a prediction mode to which a predicted value is not assigned may bewritten as “not available” in the table.

Assignment of values of the prediction modes may be determined inaccordance with the distance from the three-dimensional point to beencoded. For example, prediction mode values indicating a plurality ofprediction modes decrease with decreasing distance from thethree-dimensional point to be encoded to the surroundingthree-dimensional points having the attribute information items used asthe predicted values. The example in FIG. 49 shows that points b1, a2,a1, a0 are sequentially located closer to point b2 as thethree-dimensional point to be encoded. For example, in the calculatingof the predicted value, the attribute information item of point b1 iscalculated as the predicted value in a prediction mode indicated by aprediction mode value of “1” among two or more prediction modes, and theattribute information item of point a2 is calculated as the predictedvalue in a prediction mode indicated by a prediction mode value of “2”.As such, the prediction mode value indicating the prediction mode forcalculating, as the predicted value, the attribute information item ofpoint b1 is smaller than the prediction mode value indicating theprediction mode for calculating, as the predicted value, the attributeinformation item of point a2 farther from point b2 than point b1.

Thus, a small prediction mode value can be assigned to a point that ismore likely to be predicted and selected due to a short distance,thereby reducing a bit number for encoding the prediction mode value.Also, a small prediction mode value may be preferentially assigned to athree-dimensional point belonging to the same LoD as thethree-dimensional point to be encoded.

FIG. 52 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the third example is an example of a casewhere an attribute information item used as a predicted value is a valueof color information (YUV) of a surrounding three-dimensional point. Assuch, the attribute information item used as the predicted value may becolor information indicating a color of the three-dimensional point.

As shown in FIG. 52 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is an average of Y, U, and Vcomponents defining a YUV color space. Specifically, the predicted valueincludes a weighted average Yave of Y component values Yb1, Ya2, Ya1,Ya0 corresponding to points b1, a2, a1, a0, respectively, a weightedaverage Uave of U component values Ub1, Ua2, Ua1, Ua0 corresponding topoints b1, a2, a1, a0, respectively, and a weighted average Vave of Vcomponent values Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1,a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” include colorinformation of the surrounding three-dimensional points b1, a2, a1, a0.The color information is indicated by combinations of the Y, U, and Vcomponent values.

In FIG. 52 , the color information is indicated by a value defined bythe YUV color space, but not limited to the YUV color space. The colorinformation may be indicated by a value defined by an RGB color space ora value defined by any other color space.

As such, in the calculating of the predicted value, two or more averagesor two or more attribute information items may be calculated as thepredicted values of the prediction modes. The two or more averages orthe two or more attribute information items may indicate two or morecomponent values each defining a color space.

For example, when a prediction mode indicated by a prediction mode valueof “2” in the table in FIG. 52 is selected, a Y component, a Ucomponent, and a V component as attribute values of thethree-dimensional point to be encoded may be encoded as predicted valuesYa2, Ua2, Va2. In this case, the prediction mode value of “2” isappended to the bitstream.

FIG. 53 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the fourth example is an example of a casewhere an attribute information item used as a predicted value is a valueof reflectance information of a surrounding three-dimensional point. Thereflectance information is, for example, information indicatingreflectance R.

As shown in FIG. 53 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is weighted average Rave ofreflectances Rb1, Ra2, Ra1, Ra0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4” are reflectances Rb1, Ra2, Ra1,Ra0 of surrounding three-dimensional points b1, a2, a1, a0,respectively.

For example, when a prediction mode indicated by a prediction mode valueof “3” in the table in FIG. 53 is selected, a reflectance as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ra1. In this case, the prediction mode valueof “3” is appended to the bitstream.

As shown in FIGS. 52 and 53 , the attribute information item may includea first attribute information item and a second attribute informationitem different from the first attribute information item. The firstattribute information item is, for example, color information. Thesecond attribute information item is, for example, reflectanceinformation. In the calculating of the predicted value, a firstpredicted value may be calculated by using the first attributeinformation item, and a second predicted value may be calculated byusing the second attribute information item.

When the attribute information item includes a plurality of componentslike color information such as a YUV color space or an RGB color space,predicted values may be calculated in different prediction modes for therespective components. For example, for the YUV space, predicted valuesusing a Y component, a U component, and a V component may be calculatedin prediction modes selected for the respective components. For example,prediction mode values may be selected in prediction mode Y forcalculating the predicted value using the Y component, prediction mode Ufor calculating the predicted value using the U component, andprediction mode V for calculating the predicted value using the Vcomponent. In this case, values in tables in FIGS. 54 to 56 describedlater may be used as the prediction mode values indicating theprediction modes of the components, and the prediction mode values maybe appended to the bitstream. The YUV color space has been describedabove, but the same applies to the RGB color space.

Predicted values including two or more components among a plurality ofcomponents of an attribute information item may be calculated in acommon prediction mode. For example, for the YUV color space, predictionmode values may be selected in prediction mode Y for calculating thepredicted value using the Y component and prediction mode UV forcalculating the predicted value using the UV components. In this case,values in tables in FIGS. 54 and 57 described later may be used as theprediction mode values indicating the prediction modes of thecomponents, and the prediction mode values may be appended to thebitstream.

FIG. 54 is a diagram showing a fifth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the fifth example is an example of a casewhere an attribute information item used as a predicted value is a Ycomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 54 , a predicted value calculated in prediction mode Yindicated by a prediction mode value of “0” is weighted average Yave ofY component values Yb1, Ya2, Ya1, Ya0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are Y component valuesYb1, Ya2, Ya1, Ya0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode Y indicated by a prediction mode valueof “2” in the table in FIG. 54 is selected, a Y component as anattribute value of a three-dimensional point to be encoded may be usedas predicted value Ya2 and encoded. In this case, the prediction modevalue of “2” is appended to the bitstream.

FIG. 55 is a diagram showing a sixth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the sixth example is an example of a casewhere an attribute information item used as a predicted value is a Ucomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 55 , a predicted value calculated in prediction mode Uindicated by a prediction mode value of “0” is weighted average Uave ofU component values Ub1, Ua2, Ua1, Ua0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are U component valuesUb1, Ua2, Ua1, Ua0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode U indicated by a prediction mode valueof “1” in the table in FIG. 55 is selected, a U component as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ub1. In this case, the prediction mode valueof “1” is appended to the bitstream.

FIG. 56 is a diagram showing a seventh example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the seventh example is an example of a casewhere an attribute information item used as a predicted value is a Vcomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 56 , a predicted value calculated in prediction mode Vindicated by a prediction mode value of “0” is weighted average Vave ofV component values Vb1, Va2, Va1, Va0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are V component valuesVb1, Va2, Va1, Va0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode V indicated by a prediction mode valueof “4” in the table in FIG. 56 is selected, a V component as anattribute value of a three-dimensional point to be encoded may be usedas predicted value Va0 and encoded. In this case, the prediction modevalue of “4” is appended to the bitstream.

FIG. 57 is a diagram showing an eighth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 4. Specifically, the eighth example is an example of a casewhere attribute information items used as predicted values are a Ucomponent value and a V component value of color information of asurrounding three-dimensional point.

As shown in FIG. 57 , a predicted value calculated in prediction mode Uindicated by a prediction mode value of “0” includes weighted averageUave of U component values Ub1, Ub2, Ua1, Ua0 corresponding to pointsb1, a2, a1, a0, respectively and weighted average Vave of V componentvalues Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4” include U component values and Vcomponent values of surrounding three-dimensional points b1, a2, a1, a0,respectively.

For example, when prediction mode UV indicated by a prediction modevalue of “1” in the table in FIG. 57 is selected, a U component and a Vcomponent as attribute values of a three-dimensional point to be encodedmay be used as predicted values Ub1, Vb1 and encoded. In this case, theprediction mode value of “1” is appended to the bitstream.

A prediction mode in encoding may be selected by RD optimization. Forexample, it is conceivable that cost cost(P) when certain predictionmode P is selected is calculated, and prediction mode P with minimumcost(P) is selected. Cost cost(P) may be calculated by Equation D1using, for example, prediction residual residual(P) when a predictedvalue of prediction mode P is used, bit number bit(P) required forencoding prediction mode P, and adjustment parameter value λ.

cost(P)=abs(residual(P))+λ×bit(P)  (Equation D1)

where abs(x) denotes an absolute value of x.

A square value of x may be used instead of abs(x).

Using Equation D1 above allows selection of a prediction modeconsidering balance between a size of a prediction residual and a bitnumber required for encoding the prediction mode. Different adjustmentparameter values λ may be set in accordance with values of aquantization scale. For example, at a small quantization scale (at ahigh bit rate), value λ may be set smaller to select a prediction modewith smaller prediction residual residual(P) to increase predictionaccuracy as much as possible. At a large quantization scale (at a lowbit rate), value λ may be set larger to select an appropriate predictionmode while considering bit number bit(P) required for encodingprediction mode P.

“At a small quantization scale” is, for example, when the quantizationscale is smaller than a first quantization scale. “At a largequantization scale” is, for example, when the quantization scale islarger than a second quantization scale equal to or greater than thefirst quantization scale. Values λ may be set smaller at smallerquantization scales.

Prediction residual residual(P) is calculated by subtracting a predictedvalue of prediction mode P from an attribute value of athree-dimensional point to be encoded. Instead of prediction residualresidual(P) in calculating of cost, prediction residual residual(P) maybe quantized or inverse quantized and added to the predicted value toobtain a decoded value, and a difference (encoding error) from a decodedvalue when an attribute value of an original three-dimensional point andprediction mode P are used may be reflected in the cost value. Thisallows selection of a prediction mode with a small encoding error.

In binarizing and encoding a prediction mode, for example, a binarizedbit number may be used as bit number bit(P) required for encodingprediction mode P. For example, with number M of prediction modes being5, as in FIG. 58 , a prediction mode value indicating the predictionmode may be binarized by a truncated unary code in accordance withnumber M of prediction modes with a maximum value of 5. In this case, 1bit for a prediction mode value of “0”, 2 bits for a prediction modevalue of “1”, 3 bits for a prediction mode value of “2”, and 4 bits forprediction mode values of “4” and “5” are used as bit numbers bit(P)required for encoding the prediction mode values. By using the truncatedunary code, the bit number is set smaller for smaller prediction modevalues. This can reduce an encoding amount of a prediction mode valueindicating a prediction mode for calculating a predicted value that ismore likely to be selected, for example, that is more likely to minimizecost(P), such as an average value calculated as a predicted value whenthe prediction mode value is “0”, or an attribute information item of athree-dimensional point calculated as a predicted value when theprediction mode value is “1”, that is, an attribute information item ofa three-dimensional point close to the three-dimensional point to beencoded.

When a maximum value of the number of prediction modes is notdetermined, as in FIG. 59 , a prediction mode value indicating aprediction mode may be binarized by a unary code. When a probability ofappearance of each prediction mode is close, as shown in FIG. 60 , theprediction mode value indicating the prediction mode may be binarized bya fixed code to reduce an encoding amount.

As bit number bit(P) required for encoding the prediction mode valueindicating prediction mode P, binarized data of the prediction modevalue indicating prediction mode P may be arithmetic-encoded, and anencoding amount after the arithmetic-encoding may be used as a value ofbit(P). This allows calculation of cost using more accurate required bitnumber bit(P), thereby allowing selection of a more appropriateprediction mode.

FIG. 58 is a diagram showing a first example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 4. Specifically, the first example is an example ofbinarizing the prediction mode values with a truncated unary code withnumber M of prediction modes being 5.

FIG. 59 is a diagram showing a second example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 4. Specifically, the second example is an example ofbinarizing the prediction mode values with a unary code with number M ofprediction modes being 5.

FIG. 60 is a diagram showing a third example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 4. Specifically, the third example is an example ofbinarizing the prediction mode values with a fixed code with number M ofprediction modes being 5.

A prediction mode value indicating a prediction mode (PredMode) may bearithmetic-encoded after binarized, and appended to the bitstream. Asdescribed above, the prediction mode value may be binarized, forexample, by the truncated unary code in accordance with number M ofprediction modes. In this case, a maximum bit number after binarizationof the prediction mode value is M−1.

The binary data after binarization may be arithmetic-encoded withreference to an encoding table. In this case, for example, encodingtables may be switched for encoding for each bit of the binary data,thereby improving an encoding efficiency. Also, to reduce the number ofencoding tables, among the binary data, the beginning bit one bit may beencoded with reference to encoding table A for one bit, and each ofremaining bits may be encoded with reference to encoding table B forremaining bits. For example, in encoding binarized data “1110” with aprediction mode value of “3” in FIG. 61 , the beginning bit one bit “1”may be encoded with reference to encoding table A, and each of remainingbits “110” may be encoded with reference to encoding table B.

FIG. 61 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 4. The binarization table in FIG. 61 is anexample of binarizing the prediction mode values with a truncated unarycode with number M of prediction modes being 5.

Thus, an encoding efficiency can be improved by switching the encodingtables in accordance with a bit position of the binary data whilereducing the number of encoding tables. Further, in encoding theremaining bits, arithmetic-encoding may be performed by switching theencoding tables for each bit, or decoding may be performed by switchingthe encoding tables in accordance with a result of thearithmetic-encoding.

In binarizing and encoding the prediction mode value with the truncatedunary code using number M of prediction modes, number M of predictionmodes used in the truncated unary code may be appended to a header andthe like of the bitstream so that the prediction mode can be specifiedfrom binary data decoded on a decoding side. Also, value MaxM that maybe a value of the number of prediction modes may be defined by standardsor the like, and value MaxM−M (M≤MaxM) may be appended to the header.Number M of prediction modes may be defined by a profile or a level ofstandards rather than appended to the stream.

It is considered that the prediction mode value binarized by thetruncated unary code is arithmetic-encoded by switching the encodingtables between one bit part and a remaining part as described above. Theprobability of appearance of 0 and 1 in each encoding table may beupdated in accordance with actually generated binary data. Also, theprobability of appearance of 0 and 1 in either of the encoding tablesmay be fixed. Thus, an update frequency of the probability of appearancemay be reduced to reduce a processing amount. For example, theprobability of appearance of the one bit part may be updated, and theprobability of appearance of the remaining bit part may be fixed.

FIG. 62 is a flowchart of an example of encoding a prediction mode valueaccording to Embodiment 4. FIG. 63 is a flowchart of an example ofdecoding a prediction mode value according to Embodiment 4.

As shown in FIG. 62 , in encoding the prediction mode value, first, theprediction mode value is binarized by a truncated unary code inaccordance with number M of prediction modes (S3401).

Then, binary data of the truncated unary code is arithmetic-encoded(S3402). Thus, the binary data is included as a prediction mode in thebitstream.

Also, as shown in FIG. 63 , in decoding the prediction mode value,first, the bitstream is arithmetic-decoded by using number M ofprediction modes to generate binary data of the truncated unary code(S3403).

Then, the prediction mode value is calculated from the binary data ofthe truncated unary code (S3404).

As a method of binarizing the prediction mode value indicating theprediction mode (PredMode), the example of binarizing with the truncatedunary code using number M of prediction modes has been described, butnot limited to this. For example, the prediction mode value may bebinarized by a truncated unary code in accordance with number L (L≤M) ofprediction modes assigned with predicted values. For example, withnumber M of prediction modes being 5, when two surroundingthree-dimensional points are available for prediction of a certainthree-dimensional point to be encoded, as shown in FIG. 64 , threeprediction modes are available and the remaining two prediction modesare not available in some cases. For example, as shown in FIG. 64 , withnumber M of prediction modes being 5, two three-dimensional points inthe vicinity of the three-dimensional point to be encoded are availablefor prediction, and predicted values are not assigned to predictionmodes indicated by prediction mode values of “3” and “4” in some cases.

In this case, as shown in FIG. 65 , the prediction mode value isbinarized by a truncated unary code in accordance with number L ofprediction modes assigned with predicted values as a maximum value. Thismay reduce the bit number after binarization as compared to when theprediction mode value is binarized by the truncated unary code inaccordance with number M of prediction modes. For example, in this case,L is 3, and the prediction mode value can be binarized by a truncatedunary code in accordance with a maximum value of 3 to reduce the bitnumber. As such, binarization by the truncated unary code in accordancewith number L of prediction modes assigned with predicted values as amaximum value may reduce the bit number after binarization of theprediction mode value.

The binary data after binarization may be arithmetic-encoded withreference to an encoding table. In this case, for example, encodingtables may be switched for encoding for each bit of the binary data,thereby improving an encoding efficiency. Also, to reduce the number ofencoding tables, among binarized data, the beginning bit one bit may beencoded with reference to encoding table A for one bit, and each ofremaining bits may be encoded with reference to encoding table B forremaining bits. For example, in encoding binarized data “11” with aprediction mode value of “2” in FIG. 65 , the beginning bit one bit “1”may be encoded with reference to encoding table A, and remaining bit “1”may be encoded with reference to encoding table B.

FIG. 65 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 4. The binarization table in FIG. 65 is anexample of binarizing the prediction mode values with a truncated unarycode with number L of prediction modes assigned with predicted valuesbeing 3.

Thus, an encoding efficiency can be improved by switching the encodingtables in accordance with a bit position of the binary data whilereducing the number of encoding tables. Further, in encoding theremaining bits, arithmetic-encoding may be performed by switching theencoding tables for each bit, or decoding may be performed by switchingthe encoding tables in accordance with a result of thearithmetic-encoding.

In binarizing and encoding the prediction mode value with the truncatedunary code using number L of prediction modes assigned with predictedvalues, a predicted value may be assigned to a prediction mode tocalculate number L in the same manner as in encoding and the predictionmode may be decoded by using calculated number L so that the predictionmode can be specified from binary data decoded on a decoding side.

It is considered that the prediction mode value binarized by thetruncated unary code is arithmetic-encoded by switching the encodingtables between one bit part and a remaining part as described above. Theprobability of appearance of 0 and 1 in each encoding table may beupdated in accordance with actually generated binary data. Also, theprobability of appearance of 0 and 1 in either of the encoding tablesmay be fixed. Thus, an update frequency of the probability of appearancemay be reduced to reduce a processing amount. For example, theprobability of appearance of the one bit part may be updated, and theprobability of appearance of the remaining bit part may be fixed.

FIG. 66 is flowchart of another example of encoding a prediction modevalue according to Embodiment 4. FIG. 67 is a flowchart of anotherexample of decoding a prediction mode value according to Embodiment 4.

As shown in FIG. 66 , in encoding the prediction mode value, first,number L of prediction modes assigned with predicted values iscalculated (S3411).

Then, the prediction mode value is binarized by a truncated unary codein accordance with number L (S3412).

Then, binary data of the truncated unary code is arithmetic-encoded(S3413).

Also, as shown in FIG. 67 , in decoding the prediction mode value,first, number L of prediction modes assigned with predicted values iscalculated (S3414).

Then, the bitstream is arithmetic-decoded by using number L to generatebinary data of the truncated unary code (S3415).

Then, the prediction mode value is calculated from the binary data ofthe truncated unary code (S3416).

The prediction mode value needs not be appended for all attributevalues. For example, if a certain condition is satisfied, the predictionmode may be fixed so that the prediction mode value is not appended tothe bitstream, and if the certain condition is not satisfied, theprediction mode may be selected to append the prediction mode value tothe bitstream. For example, if condition A is satisfied, the predictionmode value may be fixed at “0” and a predicted value may be calculatedfrom an average value of surrounding three-dimensional points. Ifcondition A is not satisfied, one prediction mode may be selected from aplurality of prediction modes and a prediction mode value indicating theselected prediction mode may be append to the bitstream.

Certain condition A is, for example, that calculated maximum absolutedifferential value maxdiff of attribute values (a[0] to a[N−1]) of N(encoded and decoded) three-dimensional points in the vicinity of athree-dimensional point to be encoded is smaller than threshold Thfix.When the maximum absolute differential value of the attribute values ofthe surrounding three-dimensional points is smaller than thresholdThfix, it is determined that there is a small difference between theattribute values of the three-dimensional points and selecting aprediction mode causes no difference in the predicted value. Then, theprediction mode value is fixed at “0” and the prediction mode value isnot encoded, thereby allowing generation of an appropriate predictedvalue while reducing an encoding amount for encoding the predictionmode.

Threshold Thfix may be appended to the header and the like of thebitstream, and the encoder may perform encoding while changing thresholdThfix. For example, in encoding at a high bit rate, the encoder may setthreshold Thfix smaller than that at a low bit rate and append thresholdThfix to the header and increase the case of encoding by selecting aprediction mode, and thus perform encoding so that the predictionresidual is as small as possible. Also, in encoding at the low bit rate,the encoder sets threshold Thfix larger than that at the high bit rateand appends threshold Thfix to the header, and performs encoding in afixed prediction mode. As such, increasing the case of encoding in thefixed prediction mode at the low bit rate can improve an encodingefficiency while reducing a bit amount for encoding the prediction mode.Threshold Thfix may be defined by a profile or a level of standardsrather than appended to the bitstream.

The N three-dimensional points in the vicinity of the three-dimensionalpoint to be encoded, which are used for prediction, are N encoded anddecoded three-dimensional points with a distance from thethree-dimensional point to be encoded being smaller than threshold THd.A maximum value of N may be appended as NumNeighborPoint to thebitstream. The value of N needs not always match NumNeighborPoint as inthe case where the number of surrounding encoded and decodedthree-dimensional points is smaller than NumNeighborPoint.

The example has been given in which the prediction mode value is fixedat “0” if maximum absolute differential value maxdiff of the attributevalues of the three-dimensional points in the vicinity of thethree-dimensional point to be encoded, which are used for prediction, issmaller than threshold Thfix[i]. However, not limited to this, theprediction mode value may be fixed at any of “0” to “M−1”. Theprediction mode value to be fixed may be appended to the bitstream.

FIG. 68 is a flowchart of an example of a process of determining whetheror not a prediction mode value is fixed under condition A in encodingaccording to Embodiment 4. FIG. 69 is a flowchart of an example of aprocess of determining whether the prediction mode value is fixed ordecoded under condition A in decoding according to Embodiment 4.

As shown in FIG. 68 , first, the three-dimensional data encoding devicecalculates maximum absolute differential value maxdiff of attributevalues of N three-dimensional points in the vicinity of athree-dimensional point to be encoded (S3421).

Then, the three-dimensional data encoding device determines whether ornot maximum absolute differential value maxdiff is smaller thanthreshold Thfix (S3422). Threshold Thfix may be encoded and appended tothe header and the like of the stream.

When maximum absolute differential value maxdiff is smaller thanthreshold Thfix (Yes in S3422), the three-dimensional data encodingdevice sets the prediction mode value to “0” (S3423).

When maximum absolute differential value maxdiff is equal to or greaterthan threshold Thfix (No in S3422), the three-dimensional data encodingdevice selects one prediction mode from a plurality of prediction modes(S3424). A process of selecting the prediction mode will be describedlater in detail with reference to FIG. 76 .

Then, the three-dimensional data encoding device arithmetic-encodes aprediction mode value indicating the selected prediction mode (S3425).Specifically, the three-dimensional data encoding device performs stepsS3401 and S3402 described with reference to FIG. 62 to arithmetic-encodethe prediction mode value. The three-dimensional data encoding devicemay perform arithmetic-encoding by binarizing prediction mode PredModewith a truncated unary code using the number of prediction modesassigned with predicted values. Specifically, the three-dimensional dataencoding device may perform steps S3411 to S3413 described withreference to FIG. 66 to arithmetic-encode the prediction mode value.

The three-dimensional data encoding device calculates a predicted valueof the prediction mode set in step S3423 or the prediction mode selectedin step S3425 to output the calculated predicted value (S3426). Whenusing the prediction mode value set in step S3423, the three-dimensionaldata encoding device calculates an average of attribute values of Nsurrounding three-dimensional points as a predicted value of aprediction mode indicated by the prediction mode value of “0”.

Also, as shown in FIG. 69 , first, the three-dimensional data decodingdevice calculates maximum absolute differential value maxdiff ofattribute values of N three-dimensional points in the vicinity of athree-dimensional point to be decoded (S3431). Maximum absolutedifferential value maxdiff may be calculated as shown in FIG. 70 .Maximum absolute differential value maxdiff is, for example, a maximumvalue of a plurality of calculated absolute values of differencesbetween all possible pairs when any two of the N surroundingthree-dimensional points are paired.

Then, the three-dimensional data decoding device determines whether ornot maximum absolute differential value maxdiff is smaller thanthreshold Thfix (S3432). Threshold Thfix may be set by decoding theheader and the like of the stream.

When maximum absolute differential value maxdiff is smaller thanthreshold Thfix (Yes in S3432), the three-dimensional data decodingdevice sets the prediction mode value to “0” (S3433).

When maximum absolute differential value maxdiff is equal to or greaterthan threshold Thfix (No in S3432), the three-dimensional data decodingdevice decodes the prediction mode value from the bitstream (S3434).

The three-dimensional data decoding device calculates a predicted valueof a prediction mode indicated by the prediction mode value set in stepS3433 or the prediction mode value decoded in step S3434 to output thecalculated predicted value (S3435). When using the prediction mode valueset in step S3433, the three-dimensional data decoding device calculatesan average of attribute values of N surrounding three-dimensional pointsas a predicted value of a prediction mode indicated by the predictionmode value of “0”.

FIG. 71 is a diagram showing an example of a syntax according toEmbodiment 4. NumLoD, NumNeighborPoint[i], NumPredMode[i], Thfix[i], andNumOfPoint[i] in the syntax in FIG. 71 will be sequentially described.

NumLoD indicates the number of LoDs.

NumNeighborPoint[i] indicates an upper limit value of the number ofsurrounding points used for generating a predicted value of athree-dimensional point belonging to level i. When number M ofsurrounding points is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]), the predicted value may be calculated by usingnumber M of surrounding points. When a value of NumNeighborPoint[i]needs not be divided among LoDs, one NumNeighborPoint may be appended tothe header.

NumPredMode[i] is a total number (M) of prediction modes used forpredicting an attribute value of level i. Possible value MaxM of thenumber of prediction modes may be defined by standards or the like, andvalue MaxM−M (0<M≤MaxM) may be appended to the header as NumPredMode[i]and binarized by a truncated unary code in accordance with maximum valueMaxM−1 and encoded. Number of prediction modes NumPredMode[i] may bedefined by a profile or a level of standards rather than appended to thestream. The number of prediction modes may be defined byNumNeighborPoint[i]+NumPredMode[i]. When the value of NumPredMode[i]needs not be divided among LoDs, one NumPredMode may be appended to theheader.

Thfix[i] indicates a threshold of a maximum absolute differential valuefor determining whether or not a prediction mode of level i is fixed. Ifthe maximum absolute differential value of attribute values ofsurrounding three-dimensional points used for prediction is smaller thanThfix[i], the prediction mode is fixed at 0. Thfix[i] may be defined bya profile or a level of standards rather than appended to the stream.When the value of Thfix[i] needs not be divided among LoDs, one Thfixmay be appended to the header.

NumOfPoint[i] indicates the number of three-dimensional points belongingto level i. When total number AllNumOfPoint of three-dimensional pointsis appended to another header, NumOfPoint[NumLoD−1] (the number ofthree-dimensional points belonging to the lowest level) may becalculated by the following equation:

[Math. 5]

AllNumOfPoint−Σ_(j=0) ^(NumLoD-2)NumOfPoint[j]  (Equation D2)

rather than appended to the header. This can reduce an encoding amountof the header.

As a setting example of NumPredMode[i], the value of NumPredMode[i] maybe set larger for a higher level at which prediction is more difficultdue to a longer distance between three-dimensional points belonging tothe level, thereby increasing the number of selectable prediction modes.Also, the value NumPredMode[i] may be set smaller for a lower level atwhich prediction is easy, thereby reducing a bit amount required forencoding the prediction mode. Such setting can increase the number ofselectable prediction modes to reduce a prediction residual at the upperlevel, and reduce an encoding amount of a prediction mode at the lowerlevel, thereby improving an encoding efficiency.

As a setting example of Thfix[i], the value of Thfix[i] may be setsmaller for a higher level at which prediction is difficult due to along distance from the three-dimensional point belonging to LoD, therebyincreasing the case of selecting the prediction mode. Also, the value ofThfix[i] may be set larger value for a lower level at which predictionis easy, and the prediction mode may be fixed to reduce a bit amountrequired for encoding the prediction mode. Such setting can increase thecase of selecting the prediction mode to reduce a prediction residual atthe upper level, and fix the prediction mode to reduce an encodingamount of the prediction mode at the lower level, thereby improving anencoding efficiency.

NumLoD, NumNeighborPoint[i], NumPredMode[i], Thfix[i], or NumOfPoint[i]described above may be entropy encoded and appended to the header. Forexample, the values may be binarized and arithmetic-encoded. The valuesmay be encoded at a fixed length to reduce a processing amount.

FIG. 72 is a diagram showing an example of a syntax according toEmbodiment 4. PredMode, n-bit code, and remaining code in the syntax inFIG. 72 will be sequentially described.

PredMode indicates a prediction mode for encoding and decoding anattribute value of a j-th three-dimensional point in level i. PredModeis “0” to “M−1” (M is a total number of prediction modes). When PredModeis not in the bitstream (maxdiff≥Thfix[i] && NumPredMode[i]>1 as acondition is not satisfied), the value of PredMode may be estimated as0. The value of PredMode may be estimated as any of “0” to “M−1”, notlimited to “0”. An estimated value when PredMode is not in the bitstreammay be separately appended to the header and the like. PredMode may bebinarized by a truncated unary code in accordance with the number ofprediction modes assigned with predicted values and arithmetic-encoded.

The n-bit code indicates encoded data of a prediction residual of avalue of an attribute information item. A bit length of the n-bit codedepends on a value of R_TH[i]. The bit length of the n-bit code is, forexample, 6 bits when the value of R_TH[i] is 63, and 8 bits when thevalue of R_TH[i] is 255.

The remaining code indicates encoded data encoded by Exponential-Golombamong encoded data of the prediction residual of the attributeinformation item. The remaining code is decoded when the n-bit code isequal to R_TH[i], and the value of the n-bit code is added to the valueof the remaining code to decode the prediction residual. When the n-bitcode is not equal to R_TH[i], the remaining code needs not be decoded.

Now, a flow of a process of the three-dimensional data encoding devicewill be described. FIG. 73 is a flowchart of a three-dimensional dataencoding process by the three-dimensional data encoding device accordingto Embodiment 4.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3441). For example, three-dimensional dataencoding is encoding by using an octree representation.

When a position of a three-dimensional point is changed by quantizationor the like after encoding of geometry information, thethree-dimensional data encoding device reassigns an attributeinformation item of an original three-dimensional point to thethree-dimensional point after change (S3442). For example, thethree-dimensional data encoding device performs reassignment byinterpolating a value of the attribute information item in accordancewith a change amount of the position. For example, the three-dimensionaldata encoding device detects N three-dimensional points before changeclose to the three-dimensional position after change, and performsweighted averaging of values of attribute information items of the Nthree-dimensional points. For example, the three-dimensional dataencoding device determines, in the weighted averaging, a weight based ona distance from the three-dimensional position after change to each ofthe N three-dimensional points. Then, the three-dimensional dataencoding device sets the value obtained by the weighted averaging to avalue of an attribute information item of the three-dimensional pointafter change. When two or more three-dimensional points are changed tothe same three-dimensional position by quantization or the like, thethree-dimensional data encoding device may assign an average value ofattribute information items of two or more three-dimensional pointsbefore change as a value of an attribute information item of athree-dimensional point after change.

Then, the three-dimensional data encoding device encodes the attributeinformation item (Attribute) after reassignment (S3443). For example,when encoding multiple types of attribute information items, thethree-dimensional data encoding device may sequentially encode themultiple types of attribute information items. For example, whenencoding a color and a reflectance as attribute information items, thethree-dimensional data encoding device may generate a bitstream appendedwith a color encoding result followed by a reflectance encoding result.The plurality of encoding results of the attribute information items maybe appended to the bitstream in any order, not limited to this.

The three-dimensional data encoding device may append informationindicating an encoded data start location of each attribute informationitem in the bitstream to the header and the like. Thus, thethree-dimensional data decoding device can selectively decode anattribute information item that requires decoding, thereby omitting adecoding process of an attribute information item that does not requiredecoding. This can reduce a processing amount of the three-dimensionaldata decoding device. The three-dimensional data encoding device mayconcurrently encode multiple types of attribute information items, andcombine encoding results into one bitstream. Thus, the three-dimensionaldata encoding device can quickly encode the multiple types of attributeinformation items.

FIG. 74 is a flowchart of an attribute information item encoding process(S3443) according to Embodiment 4. First, the three-dimensional dataencoding device sets LoD (S3451). Specifically, the three-dimensionaldata encoding device assigns each three-dimensional point to any of aplurality of LoDs.

Then, the three-dimensional data encoding device starts a loop for eachLoD (S3452). Specifically, the three-dimensional data encoding devicerepeats processes in steps S3453 to S3461 for each LoD.

Then, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3453). Specifically, the three-dimensionaldata encoding device repeats processes in steps S3454 to S3460 for eachthree-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof surrounding points that are three-dimensional points in the vicinityof a target three-dimensional point to be processed and used forcalculating a predicted value of the target three-dimensional point(S3454).

Then, the three-dimensional data encoding device calculates predictedvalue P of the target three-dimensional point (S3455). A specificexample of a calculation process of predicted value P will be describedlater with reference to FIG. 75 .

Then, the three-dimensional data encoding device calculates, as aprediction residual, a difference between an attribute information itemof the target three-dimensional point and the predicted value (S3456).

Then, the three-dimensional data encoding device quantizes theprediction residual to calculate a quantized value (S3457).

Then, the three-dimensional data encoding device arithmetic-encodes thequantized value (S3458).

The three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3459).

Then, the three-dimensional data encoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S3460).

Then, the three-dimensional data encoding device finishes the loop foreach three-dimensional point (S3461).

The three-dimensional data encoding device also finishes the loop foreach LoD (S3462).

FIG. 75 is a flowchart of a predicted value calculation process (S3455)by the three-dimensional data encoding device according to Embodiment 4.

First, the three-dimensional data encoding device calculates a weightedaverage of attribute values of the N three-dimensional points in thevicinity of the target three-dimensional point to be processed, whichcan be used for predicting a predicted value of the targetthree-dimensional point, and assigns the calculated weighted average toa prediction mode indicated by a prediction mode value of “0” (S3420).

Then, the three-dimensional data encoding device performs steps S3421 toS3426 described with reference to FIG. 68 to output a predicted value ofthe target three-dimensional point.

FIG. 76 is a flowchart of a prediction mode selection process (S3424)according to Embodiment 4.

First, the three-dimensional data encoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to prediction mode values of “1” to“N” in one increment sequentially from a three-dimensional point closerto the target three-dimensional point (S3427). The prediction modevalues of “0” to “N” are assigned, and thus a total of N+1 predictionmodes are generated. When N+1 is larger than maximum number M ofprediction modes (NumPredMode) appended to the bitstream, thethree-dimensional data encoding device may generate up to M predictionmodes.

Then, the three-dimensional data encoding device calculates cost of eachprediction mode, and selects a prediction mode with minimum cost(S3428).

FIG. 77 is a flowchart of a prediction mode selection process (S3428)that minimizes cost according to Embodiment 4.

First, the three-dimensional data encoding device sets i=0 and mincost=∞as initial values (S3471). The set initial values of i and mincost arestored in a memory of the three-dimensional data encoding device.

Then, the three-dimensional data encoding device calculates cost cost[i]of i-th prediction mode PredMode[i] using, for example, Equation D1(S3472).

Then, the three-dimensional data encoding device determines whether ornot calculated cost cost[i] is smaller than mincost stored in the memory(S3473).

Then, when calculated cost cost[i] is smaller than mincost stored in thememory (Yes in S3473), the three-dimensional data encoding device setsmincost=cost[i], sets the prediction mode to predmode[i] (S3474), andgoes to step S3475. Specifically, the value of mincost stored in thememory is updated to the value of cost[i], and predmode[i] is stored asa prediction mode in the memory.

When, calculated cost cost[i] is equal to or greater than mincost storedin the memory (No in S3473), the three-dimensional data encoding devicegoes to step S3475.

Then, the three-dimensional data encoding device increments the value ofi by one (S3475).

Then, the three-dimensional data encoding device determines whether ornot i is smaller than the number of prediction modes (S3476).

When i is smaller than the number of prediction modes (Yes in S3476),the three-dimensional data encoding device returns to step S3472, andwhen not (No in S3476), the three-dimensional data encoding devicefinishes the process of selecting the prediction mode that minimizescost.

Now, a flow of a process of the three-dimensional data decoding devicewill be described. FIG. 78 is a flowchart of a three-dimensional datadecoding process by the three-dimensional data decoding device accordingto Embodiment 4. First, the three-dimensional data decoding devicedecodes the geometry information (geometry) from the bitstream (S3444).For example, the three-dimensional data decoding device performsdecoding by using an octree representation.

Then, the three-dimensional data decoding device decodes an attributeinformation item (Attribute) from the bitstream (S3445). For example,when decoding multiple types of attribute information items, thethree-dimensional data decoding device may sequentially decode themultiple types of attribute information items. For example, whendecoding a color and a reflectance as attribute information items, thethree-dimensional data decoding device decodes a color encoding resultand a reflectance encoding result in the order of appending to thebitstream. For example, when the bitstream is appended with the colorencoding result followed by the reflectance encoding result, thethree-dimensional data decoding device decodes the color encoding resultand then decodes the reflectance encoding result. The three-dimensionaldata decoding device may decode encoding results of the attributeinformation items appended to the bitstream in any order.

The three-dimensional data decoding device may obtain informationindicating an encoded data start location of each attribute informationitem in the bitstream by decoding the header and the like. Thus, thethree-dimensional data decoding device can selectively decode anattribute information item that requires decoding, thereby omitting adecoding process of an attribute information item that does not requiredecoding. This can reduce a processing amount of the three-dimensionaldata decoding device. The three-dimensional data decoding device mayconcurrently decode multiple types of attribute information items, andcombine decoding results into one three-dimensional point cloud. Thus,the three-dimensional data decoding device can quickly decode themultiple types of attribute information items.

FIG. 79 is a flowchart of an attribute information item decoding process(S3445) according to Embodiment 4. First, the three-dimensional datadecoding device sets LoD (S3481). Specifically, the three-dimensionaldata decoding device assigns each of a plurality of three-dimensionalpoints having decoded geometry information to any of a plurality ofLoDs. For example, this assignment method is the same as that used bythe three-dimensional data encoding device.

Then, the three-dimensional data decoding device starts a loop for eachLoD (S3482). Specifically, the three-dimensional data decoding devicerepeats processes in steps S3483 to S3489 for each LoD.

Then, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3483). Specifically, the three-dimensionaldata decoding device repeats processes in steps S3484 to S3488 for eachthree-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof surrounding points that are three-dimensional points in the vicinityof a target three-dimensional point to be processed and used forcalculating a predicted value of the target three-dimensional point(S3484). This process is the same as that by the three-dimensional dataencoding device.

Then, the three-dimensional data decoding device calculates predictedvalue P of the target three-dimensional point (S3485).

Then, the three-dimensional data decoding device arithmetic-decodes aquantized value from the bitstream (S3486).

The three-dimensional data decoding device also inverse quantizes thedecoded quantized value to calculate an inverse quantized value (S3487).

Then, the three-dimensional data decoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S3488).

Then, the three-dimensional data decoding device finishes the loop foreach three-dimensional point (S3489).

The three-dimensional data decoding device also finishes the loop foreach LoD (S3490).

FIG. 80 is a flowchart of a predicted value calculation process (S3485)by the three-dimensional data decoding device according to Embodiment 4.

First, the three-dimensional data decoding device calculates a weightedaverage of attribute values of the N three-dimensional points in thevicinity of the target three-dimensional point to be processed, whichcan be used for predicting a predicted value of the targetthree-dimensional point, and assigns the calculated weighted average toa prediction mode indicated by a prediction mode value of “0” (S3430).

Then, the three-dimensional data decoding device performs steps S3431 toS3435 described with reference to FIG. 69 to output a predicted value ofthe target three-dimensional point.

Instead of performing step S3430, after it is determined Yes in stepS3432 or when the prediction mode value decoded in step S3434 is “0”,the weighted average of the attribute values of the N three-dimensionalpoints in the vicinity of the target three-dimensional point to beprocessed, which can be used for predicting a predicted value of thetarget three-dimensional point, may be calculated as a predicted value.This eliminates the need to calculate the average value in predictionmodes other than the prediction mode indicated by the prediction modevalue of “0”, thereby reducing a processing amount.

FIG. 81 is a flowchart of a prediction mode decoding process (S3434)according to Embodiment 4.

First, the three-dimensional data decoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to prediction mode values of “1” to“N” in one increment sequentially from a three-dimensional point closerto the target three-dimensional point (S3491). The prediction modevalues of “0” to “N” are assigned, and thus a total of N+1 predictionmodes are generated. When N+1 is larger than maximum number M ofprediction modes (NumPredMode) appended to the bitstream, thethree-dimensional data decoding device may generate up to M predictionmodes.

Then, the three-dimensional data decoding device arithmetic-decodes theprediction mode by using the number of prediction modes (S3492).Specifically, the three-dimensional data decoding device may performsteps S3403 and S3404 described with reference to FIG. 63 toarithmetic-decode the prediction mode. The three-dimensional datadecoding device may also perform steps S3414 to S3416 described withreference to FIG. 67 to arithmetic-decode the prediction mode.

FIG. 82 is a block diagram showing a configuration of attributeinformation encoder 3100 included in the three-dimensional data encodingdevice according to Embodiment 4. FIG. 82 shows an attribute informationencoder in detail among a geometry information encoder, an attributeinformation item reassigner, and the attribute information encoderincluded in the three-dimensional data encoding device.

Attribute information encoder 3400 includes LoD generator 3401,surrounding searcher 3402, predictor 3403, prediction residualcalculator 3404, quantizer 3405, arithmetic encoder 3406, inversequantizer 3407, decoded value generator 3408, and memory 3409.

LoD generator 3401 generates LoD by using geometry information(geometry) of a three-dimensional point.

Surrounding searcher 3402 searches a neighboring three-dimensional pointadjacent to each three-dimensional point by using an LoD generationresult by LoD generator 3401 and distance information indicating adistance between three-dimensional points.

Predictor 3403 generates a predicted value of an attribute informationitem of a target three-dimensional point to be encoded. Specifically,predictor 3403 assigns predicted values to prediction modes indicated byprediction mode values of “0” to “M−1”, and selects a prediction mode.Predictor 3403 outputs the selected prediction mode, specifically, theprediction mode value indicating the prediction mode to the arithmeticencoder. Predictor 3403 performs, for example, the process in stepS3455.

Prediction residual calculator 3404 calculates (generates) a predictionresidual of the predicted value of the attribute information itemgenerated by predictor 3403. Prediction residual calculator 3404performs the process in step S3456.

Quantizer 3405 quantizes the prediction residual of the attributeinformation item calculated by prediction residual calculator 3404.

Arithmetic encoder 3106 arithmetic-encodes the prediction residualquantized by quantizer 3105. Arithmetic encoder 3406 outputs a bitstreamincluding the arithmetic-encoded prediction residual, for example, tothe three-dimensional data decoding device.

The prediction residual may be binarized, for example, by quantizer 3405before arithmetic-encoded by arithmetic encoder 3406. Arithmetic encoder3406 may generate and encode various header information items (headerinformation). Arithmetic encoder 3406 may also obtain a prediction modeused for encoding from Prediction block, and arithmetic-encode andappend the prediction mode to the bitstream.

Inverse quantizer 3407 inverse quantizes the prediction residualquantized by quantizer 3405. Inverse quantizer 3407 performs the processin step S3459.

Decoded value generator 3408 adds the predicted value of the attributeinformation item generated by predictor 3403 to the prediction residualinverse quantized by inverse quantizer 3407 to generate a decoded value.

Memory 3409 stores a decoded value of an attribute information item ofeach three-dimensional point decoded by decoded value generator 3408.For example, when generating a predicted value of a three-dimensionalpoint having been not yet encoded, predictor 3403 generates a predictedvalue by using the decoded value of the attribute information item ofeach three-dimensional point stored in memory 3409.

FIG. 83 is a block diagram of a configuration of attribute informationdecoder 3410 included in the three-dimensional data decoding deviceaccording to Embodiment 4. FIG. 83 shows an attribute informationdecoder in detail among a geometry information decoder and the attributeinformation decoder included in the three-dimensional data decodingdevice.

Attribute information decoder 3410 includes LoD generator 3411,surrounding searcher 3412, predictor 3413, arithmetic decoder 3414,inverse quantizer 3415, decoded value generator 3416, and memory 3417.

LoD generator 3411 generates LoD by using geometry information (geometryinformation) of a three-dimensional point decoded by a geometryinformation decoder (not shown).

Surrounding searcher 3412 searches a neighboring three-dimensional pointadjacent to each three-dimensional point by using an LoD generationresult by LoD generator 3411 and distance information indicating adistance between three-dimensional points.

Predictor 3413 generates a predicted value of an attribute informationitem of a target three-dimensional point to be decoded. Predictor 3413performs, for example, the process in step S3485.

Arithmetic decoder 3414 arithmetic-decodes the prediction residual inthe bitstream obtained by attribute information encoder 3400. Arithmeticdecoder 3414 may decode various header information items. Arithmeticdecoder 3414 may also output an arithmetic-decoded prediction mode topredictor 3413. In this case, predictor 3413 may calculate a predictedvalue by using the prediction mode obtained by arithmetic decoding byarithmetic decoder 3414.

Inverse quantizer 3415 inverse quantizes the prediction residualarithmetic-decoded by arithmetic decoder 3414.

Decoded value generator 3416 adds the predicted value generated bypredictor 3413 and the prediction residual inverse quantized by inversequantizer 3415 to generate a decoded value. Decoded value generator 3416outputs decoded attribute information data to any other device.

Memory 3417 stores a decoded value of an attribute information item ofeach three-dimensional point decoded by decoded value generator 3416.For example, when generating a predicted value of a three-dimensionalpoint having been not yet decoded, predictor 3413 generates a predictedvalue by using the decoded value of the attribute information item ofeach three-dimensional point stored in memory 3417;

Embodiment 5

As described above, the three-dimensional data encoding devicecalculates a maximum absolute differential value of attribute values ofN three-dimensional points (surrounding three-dimensional points) in thevicinity of a target three-dimensional point to be encoded, which can beused for prediction by the three-dimensional data encoding device andthe three-dimensional data decoding device (that is, a maximum absolutevalue of a difference between attribute values of any twothree-dimensional points among the N three-dimensional points). Thethree-dimensional data encoding device also switches between fixing aprediction mode in accordance with the calculated maximum absolutedifferential value, that is, using an arbitrarily predeterminedprediction mode, and selecting and appending a prediction mode to thebitstream.

However, the three-dimensional data encoding device needs not switchbetween fixing a prediction mode in accordance with the maximum absolutedifferential value, and selecting and appending a prediction mode to thebitstream. For example, the three-dimensional data encoding device mayselect whether to fix the prediction mode under the condition describedabove or select the prediction mode, and append the result as aprediction mode fixing flag to the bitstream.

Thus, the three-dimensional data decoding device can decode theprediction mode fixing flag appended to the bitstream to determinewhether the three-dimensional data encoding device has fixed theprediction mode or has selected and encoded the prediction mode.

When the three-dimensional data encoding device has fixed the predictionmode, the three-dimensional data decoding device can determine that theprediction mode is not encoded in the bitstream. When thethree-dimensional data encoding device has selected the prediction mode,the three-dimensional data decoding device can determine that theprediction mode in the bitstream requires decoding. Thus, thethree-dimensional data decoding device can correctly decode the encodedprediction mode included in the bitstream.

Also, the three-dimensional data decoding device can arithmetic-decodethe prediction mode without calculating a maximum absolute differentialvalue of attribute values of N three-dimensional points in the vicinityof a target three-dimensional point to be encoded, which can be used forprediction. Thus, the three-dimensional data decoding device canconcurrently perform arithmetic-decoding of the bitstream and LoDgeneration and the like. As a result, the three-dimensional datadecoding device can increase throughput of the entire process.

The three-dimensional data encoding device may append a prediction modefixing flag for each three-dimensional point.

Thus, the three-dimensional data decoding device can switch betweenfixing the prediction mode and selecting the prediction mode for eachthree-dimensional point in accordance with the prediction mode fixingflag. Thus, the three-dimensional data encoding device can improve anencoding efficiency.

The three-dimensional data encoding device may set a prediction modefixing flag for each LoD. For example, the three-dimensional dataencoding device sets the prediction mode fixing flag to 0 to select aprediction mode for an upper level, among LoDs, at which prediction isdifficult, and sets the prediction mode fixing flag to 1 to fix theprediction mode for a lower level at which prediction is easy. Thus, thethree-dimensional data encoding device can reduce an encoding amount forappending the prediction mode.

FIG. 84 is a flowchart of a prediction mode determination processperformed by the three-dimensional data encoding device according tothis embodiment.

First, the three-dimensional data encoding device calculates maximumabsolute differential value maxdiff of attribute values of Nthree-dimensional points in the vicinity of a target three-dimensionalpoint to be encoded (S3501).

FIG. 85 is a diagram showing an example of a syntax of the predictionmode determination process performed by the three-dimensional dataencoding device according to this embodiment. Specifically, FIG. 85shows an example of a syntax in step S3501 shown in FIG. 84 .

In FIG. 85 , the attribute values of the N three-dimensional points inthe vicinity of the target three-dimensional point to be encoded aredenoted by a[0] to a[N−1], and the maximum absolute differential valueis denoted by maxdiff. The attribute values of the N three-dimensionalpoints in the vicinity of the target three-dimensional point to beencoded are attribute values encoded by the three-dimensional dataencoding device and decoded by the three-dimensional data decodingdevice.

The three-dimensional data encoding device calculates maximum absolutedifferential value maxdiff, for example, by the example of the syntaxshown in FIG. 85 .

The N three-dimensional points in the vicinity of the targetthree-dimensional point to be encoded, which are used for prediction bythe three-dimensional data encoding device, are N encoded and decodedthree-dimensional points with a distance from the targetthree-dimensional point to be encoded shorter than threshold THd.

Here, as described above, the three-dimensional data encoding device mayappend a maximum value of N as NumNeighborPoint to the bitstream. Thevalue of N needs not match NumNeighborPoint as in the case where thenumber of encoded and decoded three-dimensional points in attributeinformation items (pieces of attribute information) of thethree-dimensional points in the vicinity of the target three-dimensionalpoint to be encoded is smaller than NumNeighborPoint.

Again with reference to FIG. 84 , the three-dimensional data encodingdevice then determines whether or not maxdiff<Thfix is satisfied(S3502). Thfix is an arbitrarily predetermined constant.

When determining that maxdiff<Thfix is satisfied (Yes in S3502), thethree-dimensional data encoding device performs arithmetic-encoding witha prediction mode fixing flag (fixedPredMode) being 1 (S3503).

Then, the three-dimensional data encoding device sets a prediction modevalue (PredMode/hereinafter also simply referred to as a predictionmode) to 0 (S3504).

When determining that maxdiff<Thfix is not satisfied (No in S3502), thethree-dimensional data encoding device performs arithmetic-encoding withthe prediction mode fixing flag being 0 (S3505).

Then, the three-dimensional data encoding device selects a predictionmode (S3506).

Then, the three-dimensional data encoding device arithmetic-encodes theselected prediction mode (S3507).

The example has been given above in which the three-dimensional dataencoding device fixes the prediction mode at 0 if the maximum absolutedifferential value of the attribute values of the three-dimensionalpoints in the vicinity of the target three-dimensional point to beencoded, which are used for prediction, is smaller than Thfix[i], butnot limited to this. For example, the three-dimensional data encodingdevice may fix the prediction mode at any of 0 to M−1.

The three-dimensional data encoding device may append a value of a fixedprediction mode (also referred to as PredMode or mode number) to thebitstream.

FIG. 86 is a flowchart of a prediction mode determination processperformed by the three-dimensional data decoding device according tothis embodiment.

First, the three-dimensional data decoding device arithmetic-decodes anencoded prediction mode fixing flag included in the bitstream (S3511).

Then, the three-dimensional data decoding device determines whether ornot the prediction mode fixing flag==1 is satisfied (S3512).

When determining that the prediction mode fixing flag==1 is satisfied(Yes in S3512), the three-dimensional data decoding device sets theprediction mode value to 0 (S3513).

When determining that the prediction mode fixing flag==1 is notsatisfied (No in S3512), the three-dimensional data decoding devicedecodes the prediction mode value from the bitstream and determines theprediction mode value (S3514).

The three-dimensional data decoding device determines a predicted valuein accordance with the determined prediction mode.

The prediction mode fixing flag may be provided in any position.

FIG. 87 is a diagram showing an example of a syntax of attribute datawhen a prediction mode fixing flag is provided for eachthree-dimensional point.

fixedPredMode is a flag indicating if the three-dimensional dataencoding device fixes a prediction mode. For example, when a value offixedPredMode is 1, the three-dimensional data encoding device may fix aprediction mode, and when the value of fixedPredMode is 0, thethree-dimensional data encoding device may select a prediction mode.

fixedPredMode may be also set for each LoD as in FIG. 88 .

FIG. 88 is a diagram showing an example of a syntax of attribute datawhen a prediction mode fixing flag is provided for each LoD.

fixedPredMode[i] is a flag indicating if a prediction mode of level i ofLoD is fixed.

PredMode is a value indicating a prediction mode for encoding anddecoding an attribute value of a j-th three-dimensional point at leveli. PredMode takes any of 0 to M−1 (M is a total number of predictionmodes). When PredMode is not in the bitstream (specifically, when!fixedPredMode&&NumPredMode[i]>1 is not satisfied), thethree-dimensional data decoding device may estimate PredMode as 0.

When PredMode is not in the bitstream, the three-dimensional datadecoding device needs not set PredMode to 0, and may use any of 0 to M−1as an estimated value.

The three-dimensional data encoding device may separately append, to aheader and the like, the estimated value when PredMode is not in thebitstream.

As described above, the three-dimensional data encoding device maybinarize PredMode by truncated unary coding using total number M ofprediction modes, and arithmetic-encode the binarized value.

The truncated unary coding is one method of binarization. By using thetruncated unary coding, for a multivalued signal taking a value otherthan a maximum value, a signal is generated including the same number ofis as indicated by the multivalued signal, with 0 at the end. For amultivalued signal taking a maximum value, the maximum value ispreviously set, and a signal is generated including the same number ofis as indicated by the multivalued signal (without 0 at the end). Forexample, as described above, when the number of prediction modes is 5,and the three-dimensional data encoding device binarizes each of theprediction modes by using the truncated unary coding, prediction modes 0to 4 are sequentially represented by 0, 10, 110, 1110, 1111. As such,the three-dimensional data encoding device binarizes the prediction modeby the truncated unary coding using the number of prediction modes (thatis, in accordance with the number of prediction modes).

By using the truncated unary coding, for a multivalued signal taking avalue other than the maximum value, a signal may be generated includingthe same number of 0s as indicated by the multivalued signal, with 1 atthe end. Specifically, 0(s) and 1(s) described above may be inverted.

The three-dimensional data encoding device may arithmetic-encode, asNumPredMode, the value of total number M of prediction modes, and appendNumPredMode to the header.

NumPredMode is a value indicating the total number of prediction modes.

Thus, the three-dimensional data decoding device can decode NumPredModein the header to calculate total number M of prediction modes, anddecode PredMode in accordance with total number M of prediction modes.Thus, the three-dimensional data decoding device can set (generate) LoD,calculate three-dimensional points in the vicinity of a targetthree-dimensional point to be decoded, which can be used for prediction,and perform arithmetic-decoding of the bitstream without waiting forcalculation of the number of prediction modes assigned with predictedvalues. As a result, the three-dimensional data decoding device canconcurrently perform arithmetic-decoding of the bitstream and LoDgeneration and the like, thereby increasing throughput of the entireprocess.

An n-bit code is encoded data of a prediction residual of a value of anattribute information item (attribute information). A bit length of then-bit code depends on a value of R_TH[i]. For example, the bit length is6 bits when the value of R_TH[i] is 63, and 8 bits when the value ofR_TH[i] is 255. Any value of R_TH may be previously set.

A remaining code is encoded data encoded by an Exponential-Golomb codeamong encoded data of the prediction residual of the value of theattribute information item. The three-dimensional data decoding devicedecodes the remaining code when the n-bit code is equal to R_TH[i], andadds the value of the n-bit code to the value of the remaining code todecode the prediction residual.

When the n-bit code is not equal to R_TH[i], the three-dimensional datadecoding device needs not decode the remaining code.

FIG. 89 is a flowchart of an example of a prediction mode encodingprocess by the three-dimensional data encoding device.

First, the three-dimensional data encoding device binarizes theprediction mode by the truncated unary coding using total number M ofprediction modes (S3521).

Then, the three-dimensional data encoding device arithmetic-encodesbinary data of the truncated unary coding (S3522).

Then, the three-dimensional data encoding device appends total number Mof prediction modes as NumPredMode to the header and encodes NumPredMode(S3523).

The three-dimensional data encoding device transmits a bitstreamincluding, for example, encoded NumPredMode to the three-dimensionaldata decoding device.

FIG. 90 is a flowchart of an example of a prediction mode decodingprocess by the three-dimensional data decoding device.

First, the three-dimensional data decoding device decodes encodedNumPredMode included in the bitstream to set total number M ofprediction modes (S3524).

Then, the three-dimensional data decoding device arithmetic-decodesencoded PredMode in accordance with decoded total number M of predictionmodes, and generates binary data of the truncated unary coding (S3525).

Then, the three-dimensional data decoding device calculates a predictionmode from the binary data of the truncated unary coding (S3526).

The three-dimensional data encoding device may arithmetic-encodefixedPredMode descried above with reference to an encoding table. Thethree-dimensional data encoding device may update the probability ofappearance of 0 and 1 in the encoding table in accordance with a valueof actually generated fixedPredMode. The three-dimensional data encodingdevice may fix the probability of appearance of 0 and 1 in either ofencoding tables. Thus, the three-dimensional data encoding device canreduce an update frequency of the probability of appearance of 0 and 1in the encoding table to reduce a processing amount.

FIG. 91 is a flowchart of a three-dimensional data encoding process bythe three-dimensional data encoding device according to this embodiment.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3531). For example, the three-dimensional dataencoding device may perform encoding by using an octree representation.

Then, when a position of a three-dimensional point is changed byquantization or the like after encoding of the geometry information, thethree-dimensional data encoding device reassigns an attributeinformation item of an original three-dimensional point to thethree-dimensional point after change (S3532).

The three-dimensional data encoding device may perform reassignment byinterpolating a value of the attribute information item in accordancewith a change amount of the position. For example, the three-dimensionaldata encoding device may detect N three-dimensional points before changein the vicinity of a target three-dimensional point to be encoded, whichare close to the three-dimensional position after change. Then, thethree-dimensional data encoding device may perform weighted averaging ofvalues of attribute information items of the detected Nthree-dimensional points in accordance with a distance from the targetthree-dimensional position to be encoded after change to each of the Nthree-dimensional points, and set the weighted average value as a valueof an attribute information item of the three-dimensional point afterchange. When two or more three-dimensional points are changed to thesame three-dimensional position by quantization or the like, thethree-dimensional data encoding device may assign an average value ofattribute information items of two or more three-dimensional pointsbefore change as a value of an attribute information item of athree-dimensional point after change.

Then, the three-dimensional data encoding device encodes the attributeinformation item (Attribute) after reassignment (S3533). For example,the three-dimensional data encoding device may sequentially encode aplurality of attribute information items.

Also, for example, when encoding a color and a reflectance as attributeinformation items, the three-dimensional data encoding device maygenerate a bitstream appended with a color encoding result followed by areflectance encoding result.

The three-dimensional data encoding device may append the encodingresults of the attribute information items to the bitstream in anyorder. The three-dimensional data encoding device may append, to theheader and the like, an encoded data start location of each attributeinformation item in the bitstream.

Thus, the three-dimensional data decoding device can decode an attributeinformation item that requires decoding. As a result, thethree-dimensional data decoding device can omit a decoding process of anattribute information item that does not require decoding, therebyreducing a processing amount.

The three-dimensional data encoding device may concurrently encode aplurality of attribute information items, and combine encoding resultsinto one bitstream.

Thus, the three-dimensional data encoding device can quickly encode theplurality of attribute information items.

FIG. 92 is a flowchart of an attribute information item encoding process(S3533) shown in FIG. 91 .

First, the three-dimensional data encoding device sets LoD (S35331).Specifically, the three-dimensional data encoding device assigns eachthree-dimensional point to any of a plurality of LoDs.

Then, the three-dimensional data encoding device starts a loop for eachLoD (S35332). Specifically, the three-dimensional data encoding devicerepeats processes in steps S35333 to S35341 for each LoD.

Then, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S35333). Specifically, the three-dimensionaldata encoding device repeats processes in steps S35334 to S35340 foreach three-dimensional point at a certain LoD. FIG. 92 shows encoding oftarget three-dimensional point P to be encoded.

Then, the three-dimensional data encoding device searches a plurality ofsurrounding points that are three-dimensional points in the vicinity oftarget three-dimensional point P to be processed and used forcalculating a predicted value of target three-dimensional point P(S35334).

Then, the three-dimensional data encoding device calculates a predictedvalue of target three-dimensional point P (S35335).

Then, the three-dimensional data encoding device calculates, as aprediction residual, a difference between an attribute information itemof target three-dimensional point P and the predicted value (S35336).

Then, the three-dimensional data encoding device quantizes theprediction residual to calculate a quantized value (S35337).

Then, the three-dimensional data encoding device arithmetic-encodes thequantized value (S35338).

Then, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S35339).

Then, the three-dimensional data encoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S35340).

Then, the three-dimensional data encoding device finishes the loop foreach three-dimensional point (S35341).

The three-dimensional data encoding device also finishes the loop foreach LoD (S35342).

FIG. 93 is a flowchart of a predicted-value calculation process (S35335)shown in FIG. 92 .

First, the three-dimensional data encoding device calculates a weightedaverage value of attribute values of the N three-dimensional points inthe vicinity of the target three-dimensional point to be encoded, whichcan be used for prediction, and assigns the calculated weighted averagevalue to prediction mode 0 (S353351).

Then, the three-dimensional data encoding device calculates maximumabsolute differential value maxdiff of the attribute values of the Nthree-dimensional points in the vicinity of the target three-dimensionalpoint to be encoded (S353352).

Then, the three-dimensional data encoding device determines whether ornot maximum absolute differential value maxdiff<Thfix is satisfied(S353353).

When determining that maximum absolute differential value maxdiff<Thfixis satisfied (Yes in S353353), the three-dimensional data encodingdevice performs arithmetic-encoding with a prediction mode fixing flagbeing 1 (S353354).

Then, the three-dimensional data encoding device sets the predictionmode to 0 (prediction mode for indicating the weighted average value asa predicted value) (S353355).

Then, the three-dimensional data encoding device arithmetic-encodes thepredicted value of the set prediction mode, and outputs the predictedvalue to, for example, the three-dimensional data decoding device(S353356).

When determining that maximum absolute differential value maxdiff<Thfixis not satisfied (No in S353353), the three-dimensional data encodingdevice performs arithmetic-encoding with the prediction mode fixing flagbeing 0 (S353357).

Then, the three-dimensional data encoding device selects and determinesa prediction mode (S353358).

Then, the three-dimensional data encoding device arithmetic-encodes theselected and determined prediction mode (S353359).

As described above, the three-dimensional data encoding device maybinarize prediction mode PredMode by truncated unary coding using totalnumber M of prediction modes and arithmetic-encode PredMode. Thethree-dimensional data encoding device may encode total number M ofprediction modes as NumPredMode and append NumPredMode to the header.

Thus, the three-dimensional data decoding device can decode NumPredModein the header, thereby correctly decoding prediction mode PredMode.

When NumPredMode is 1, the three-dimensional data encoding device needsnot encode PredMode. Thus, the three-dimensional data encoding devicecan reduce an encoding amount when NumPredMode is 1.

FIG. 94 is a flowchart of a prediction mode selection process (S353358)shown in FIG. 93 .

First, the three-dimensional data encoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to be encoded to prediction modes 1to N sequentially from a three-dimensional point closer to the targetthree-dimensional point (S3541). For example, the three-dimensional dataencoding device generates N+1 prediction modes. When N+1 is larger thantotal number M of prediction modes (NumPredMode) appended to thebitstream, the three-dimensional data encoding device may generate up toM prediction modes.

Then, the three-dimensional data encoding device calculates cost of eachprediction mode, and selects a prediction mode with minimum cost(S3542).

FIG. 95 is a flowchart of a specific example of the prediction modeselection process (S3542) shown in FIG. 94 .

First, the three-dimensional data encoding device sets i=0 and mincost=∞(S35421).

Then, the three-dimensional data encoding device calculates cost(cost[i]) of i-th prediction mode PredMode[i] (S35422).

Then, the three-dimensional data encoding device determines whether ornot cost[i]<mincost is satisfied (S35423).

Then, when determining that cost[i]<mincost is satisfied (Yes inS35423), the three-dimensional data encoding device setsmincost=cost[i], and sets the prediction mode to PredMode[i] (S35424).

Following step S35424 or when determining that cost[i]<mincost is notsatisfied (No in S35423), the three-dimensional data encoding devicesets i=i+1 (S35425).

Then, the three-dimensional data encoding device determines whether ornot i<the number of prediction modes (total number of prediction modes)is satisfied (S35426).

When determining that i<the number of prediction modes is not satisfied(No in S35426), the three-dimensional data encoding device finishes theselection process. When determining that i<the number of predictionmodes is satisfied (Yes in S35426), the three-dimensional data encodingdevice returns the process to step S35422.

FIG. 96 is a flowchart of a three-dimensional data decoding process bythe three-dimensional data decoding device according to this embodiment.

The three-dimensional data decoding device decodes geometry information(geometry) of an encoded three-dimensional point (S3551). For example,the three-dimensional data decoding device may decode the geometryinformation by using an octree representation.

Then, the three-dimensional data decoding device decodes an attributeinformation item of the encoded three-dimensional point (S3552).

The three-dimensional data decoding device may sequentially decode aplurality of attribute information items. For example, when decoding acolor and a reflectance as attribute information items, thethree-dimensional data decoding device may decode a bitstream appendedwith a color encoding result followed by a reflectance encoding resultin this order.

The three-dimensional data decoding device may decode the encodingresults of the attribute information items appended to the bitstream inany order.

The three-dimensional data decoding device may obtain an encoded datastart location of each attribute information item in the bitstream bydecoding the header and the like.

Thus, the three-dimensional data decoding device can decode an attributeinformation item that requires decoding. As a result, thethree-dimensional data decoding device can omit a decoding process of anattribute information item that does not require decoding, therebyreducing a processing amount.

The three-dimensional data decoding device may concurrently decode aplurality of attribute information items, and combine decoding resultsinto one three-dimensional point cloud.

Thus, the three-dimensional data decoding device can quickly decode theplurality of attribute information items.

FIG. 97 is a flowchart of a calculation process of a predicted value ofP (S3552) shown in FIG. 96 .

First, the three-dimensional data decoding device obtains decodedgeometry information of a three-dimensional point included in thebitstream (S35520). Specifically, the three-dimensional data decodingdevice decodes encoded geometry information of a three-dimensional pointincluded in the bitstream transmitted from the three-dimensional dataencoding device, and thus obtains the decoded geometry information.

Then, the three-dimensional data decoding device sets LoD (S35521).Specifically, the three-dimensional data decoding device assigns eachthree-dimensional point to any of a plurality of LoDs.

Then, the three-dimensional data decoding device starts a loop for eachLoD (S35522). Specifically, the three-dimensional data decoding devicerepeats processes in steps S35523 to S35528 for each LoD.

The three-dimensional data decoding device obtains an attributeinformation item included in the bitstream concurrently with, forexample, the processes in step S35520 and thereafter (S355201).

The three-dimensional data decoding device decodes PredMode and aquantized value of P (S355211).

For example, in steps in the dashed-line encircled portion (morespecifically, steps S35521 and step S355211) in FIG. 97 , thethree-dimensional data decoding device can append the number ofthree-dimensional points NumOfPoint for each LoD to the header and thelike, and thus independently perform a decoding process of PredMode inthe bitstream and a process of calculating three-dimensional points inthe vicinity of a target three-dimensional point to be decoded, whichcan be used for prediction after LoD generation. Thus, thethree-dimensional data decoding device can independently perform LoDgeneration and searching of a neighboring point of P, and anarithmetic-decoding process of PredMode, an n-bit code, and a remainingcode (decoding of PredMode and quantized value of P). Thus, thethree-dimensional data decoding device may concurrently perform theprocesses.

Thus, the three-dimensional data decoding device can reduce time for theentire process.

Then, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S35523). Specifically, the three-dimensionaldata decoding device repeats processes in steps S35524 to S35527 foreach three-dimensional point at a certain LoD. FIG. 97 shows decoding oftarget three-dimensional point P to be decoded.

Then, the three-dimensional data decoding device searches a plurality ofsurrounding points that are three-dimensional points in the vicinity oftarget three-dimensional point P to be processed and used forcalculating a predicted value of target three-dimensional point P(S35524).

Then, the three-dimensional data decoding device calculates a predictedvalue of target three-dimensional point P in accordance with PredMode(that is, a prediction mode value) decoded in step S355211 (S35525).

Then, the three-dimensional data decoding device inverse quantizes aquantized value in accordance with a quantized value of P decoded instep S355211 to calculate an inverse quantized value (S35526).

Then, the three-dimensional data decoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S35527).

Then, the three-dimensional data decoding device finishes the loop foreach three-dimensional point (S35528).

The three-dimensional data decoding device also finishes the loop foreach LoD (S35529).

FIG. 98 is a flowchart of details of the calculation process of thepredicted value of P (S35525) shown in FIG. 97 .

First, the three-dimensional data decoding device calculates a weightedaverage value of attribute values of the N three-dimensional points inthe vicinity of the target three-dimensional point to be decoded, whichcan be used for prediction, and assigns the calculated weighted averagevalue to prediction mode 0 (S355251).

Then, the three-dimensional data decoding device assigns attributeinformation items of the N three-dimensional points to prediction modes1 to N sequentially from a three-dimensional point closer to the targetthree-dimensional point to be decoded (S355252).

Then, the three-dimensional data decoding device outputs the predictedvalue of the prediction mode decoded in step S355211 shown in FIG. 97(S355253).

The three-dimensional data encoding device may calculate a weightedaverage value of attribute values of the N three-dimensional points inthe vicinity of the target three-dimensional point to be encoded aftersetting the prediction mode to 0, that is, when maxdiff<Thfix is true orthe decoded prediction mode is 0.

Thus, when the prediction mode is other than 0, the three-dimensionaldata decoding device needs not calculate a weighted average value. Thus,the three-dimensional data decoding device can reduce a processingamount.

FIG. 99 is a flowchart of a calculation process of a prediction mode anda quantized value of P (S355211) shown in FIG. 98 .

First, the three-dimensional data decoding device arithmetic-decodes aprediction mode fixing flag (S3561).

Then, the three-dimensional data decoding device determines whether ornot the arithmetic-decoded prediction mode fixing flag==1 is satisfied(S3562).

Then, when determining that the prediction mode fixing flag==1 issatisfied (Yes in S3562), the three-dimensional data decoding devicefixes the prediction mode at 0 (weighted average value) (S3563).

When determining that the prediction mode fixing flag=1 is not satisfied(No in S3562), the three-dimensional data decoding device decodes theprediction mode from the bitstream (S3564).

As described above, the three-dimensional data decoding device mayarithmetic-decode prediction mode PredMode in accordance with totalnumber M of prediction modes obtained by decoding the header of thebitstream.

When total number M of prediction modes is 1, the three-dimensional datadecoding device may estimate PredMode as 0 without decoding PredMode.

Then, the three-dimensional data decoding device determines whether ornot a value of an n-bit code==R_TH[LoDN] is satisfied (S3565).

When determining that the value of the n-bit code==R_TH[LoDN] issatisfied (Yes in S3565), the three-dimensional data decoding devicearithmetic-decodes a remaining code (S3566).

Then, the three-dimensional data decoding device calculates a value ofthe remaining code with reference to a table for an Exponential-Golombcode (S3567).

Then, the three-dimensional data decoding device sets (R_TH[LoDN]+valueof remaining code) as a prediction residual (S3568).

Then, the three-dimensional data decoding device transforms the decodedprediction residual from an unsigned integer value to a signed integervalue (S3569).

When determining that the value of the n-bit code==R_TH[LoDN] is notsatisfied (No in S3565), the three-dimensional data decoding devicedecodes the prediction mode from the bitstream (S3570), and performsstep S3569.

As described above, the three-dimensional data encoding devicecalculates a maximum absolute differential value of attribute values ofN three-dimensional points in the vicinity of a target three-dimensionalpoint to be encoded, which can be used for prediction by thethree-dimensional data encoding device and the three-dimensional datadecoding device. The three-dimensional data encoding device alsoswitches between fixing a prediction mode in accordance with thecalculated maximum absolute differential value and selecting andappending a prediction mode to the bitstream.

However, the three-dimensional data encoding device needs not switchbetween fixing a prediction mode in accordance with the maximum absolutedifferential value, and selecting and appending a prediction mode to thebitstream. For example, the three-dimensional data encoding device mayalways select a prediction mode and append the prediction mode to thebitstream.

Thus, the three-dimensional data decoding device can always decode theprediction mode appended to the bitstream, thereby correctly decodingthe bitstream.

Also, the three-dimensional data decoding device can arithmetic-decodethe prediction mode without calculating a maximum absolute differentialvalue of attribute values of N three-dimensional points in the vicinityof a target three-dimensional point to be decoded, which can be used forprediction. Thus, the three-dimensional data decoding device canconcurrently perform arithmetic-decoding of the bitstream and LoDgeneration and the like. As a result, the three-dimensional datadecoding device can increase throughput of the entire process.

When total number M of prediction modes is 1, the three-dimensional datadecoding device can estimate the prediction mode value as 0 withoutreference to the value. Thus, when total number M of prediction modes is1, the three-dimensional data encoding device needs not append theprediction mode to the bitstream.

Thus, the three-dimensional data encoding device can reduce an encodingamount when total number M of prediction modes is 1.

FIG. 100 is a flowchart of a process when the three-dimensional dataencoding device does not fix a prediction mode according to thisembodiment.

First, the three-dimensional data encoding device selects a predictionmode (S3571).

Then, the three-dimensional data encoding device arithmetic-encodes theselected prediction mode (S3572).

FIG. 101 is a flowchart of a process when the three-dimensional datadecoding device does not fix a prediction mode according to thisembodiment.

The three-dimensional data decoding device decodes the prediction modefrom the bitstream (S3573).

The three-dimensional data encoding device may always select aprediction mode by any method. For example, Thfix[i]=0 in the example ofthe syntax may be used. Thus, the three-dimensional data encoding devicecan always append PredMode to the bitstream because a minimum value ofmaxdiff is 0.

For the three-dimensional data encoding device to always append PredModeto the bitstream, Thfix[i] may be any value as long as maxdiff≥Thfix[i]is true.

FIG. 102 is a diagram showing another example of a syntax of attributedata according to this embodiment.

PredMode is a value indicating a prediction mode for encoding anddecoding an attribute value of a j-th three-dimensional point at level iof LoD. PredMode takes any of 0 to M−1 (M is a total number ofprediction modes).

When PredMode is not in the bitstream (specifically, NumPredMode[i]>1 isnot satisfied), the three-dimensional data decoding device may estimatePredMode as 0.

When PredMode is not in the bitstream, the three-dimensional datadecoding device needs not set PredMode to 0, and may use any of 0 to M−1as an estimated value.

The three-dimensional data encoding device may separately append, to theheader and the like, the estimated value when PredMode is not in thebitstream.

As described above, the three-dimensional data encoding device maybinarize PredMode by truncated unary coding using total number M ofprediction modes, and arithmetic-encode the binarized value.

The three-dimensional data encoding device may encode, as NumPredMode,the value of total number M of prediction modes, and append NumPredModeto the header of the bitstream.

Thus, the three-dimensional data decoding device can decode NumPredModein the header to calculate total number M of prediction modes, anddecode PredMode in accordance with total number M of prediction modes.As a result, the three-dimensional data decoding device can generateLoD, calculate three-dimensional points in the vicinity of a targetthree-dimensional point to be decoded, which can be used for prediction,and perform arithmetic-decoding of the bitstream without waiting forcalculation of the number of prediction modes assigned with predictedvalues.

Thus, the three-dimensional data decoding device can concurrentlyperform arithmetic-decoding of the bitstream and LoD generation and thelike. As a result, the three-dimensional data decoding device canincrease throughput of the entire process.

An n-bit code is encoded data of a prediction residual of a value of anattribute information item. A bit length of the n-bit code depends on avalue of R_TH[i]. For example, the bit length is 6 bits when the valueof R_TH[i] is 63, and 8 bits when the value of R_TH[i] is 255.

A remaining code is encoded data encoded by an Exponential-Golomb codeamong encoded data of the prediction residual of the value of theattribute information item. The three-dimensional data decoding devicedecodes the remaining code when the n-bit code is equal to R_TH[i], andadds the value of the n-bit code to the value of the remaining code todecode the prediction residual.

When the n-bit code is not equal to R_TH[i], the three-dimensional datadecoding device needs not decode the remaining code.

FIG. 103 is a flowchart of an example of a prediction mode encodingprocess by the three-dimensional data encoding device according to thisembodiment.

First, the three-dimensional data encoding device binarizes theprediction mode by the truncated unary coding using total number M ofprediction modes (S3574).

Then, the three-dimensional data encoding device arithmetic-encodesbinary data of the truncated unary coding (S3575).

Then, the three-dimensional data encoding device appends total number Mof prediction modes as NumPredMode to the header and encodes NumPredMode(S3576).

FIG. 104 is a flowchart of an example of a prediction mode decodingprocess by the three-dimensional data decoding device according to thisembodiment.

First, the three-dimensional data decoding device decodes encodedNumPredMode included in the bitstream to set total number M ofprediction modes (S3577).

Then, the three-dimensional data decoding device arithmetic-decodesencoded PredMode in accordance with decoded total number M of predictionmodes, and generates binary data of the truncated unary coding (S3578).

Then, the three-dimensional data decoding device calculates a predictionmode from the binary data of the truncated unary coding (S3579).

FIG. 105 is a flowchart of another example of the predicted-valuecalculation process (step S35335) shown in FIG. 92 .

First, the three-dimensional data encoding device calculates a weightedaverage value of attribute values of the N three-dimensional points inthe vicinity of the target three-dimensional point to be encoded, whichcan be used for prediction, and assigns the calculated weighted averagevalue to prediction mode 0 (S3581).

Then, the three-dimensional data encoding device selects and determinesa prediction mode (S3582).

Then, the three-dimensional data encoding device arithmetic-encodes theselected and determined prediction mode (S3583).

The three-dimensional data encoding device outputs a predicted value ofthe determined prediction mode (S3584).

As described above, the three-dimensional data encoding device maybinarize prediction mode PredMode in accordance with total number M ofprediction modes and arithmetic-encodes PredMode. The three-dimensionaldata encoding device may encode total number M of prediction modes asNumPredMode and append NumPredMode to the header.

Thus, the three-dimensional data decoding device can decode NumPredModein the header, thereby correctly decoding prediction mode PredMode.

When NumPredMode is 1, the three-dimensional data encoding device needsnot encode PredMode.

Thus, the three-dimensional data encoding device can reduce an encodingamount when NumPredMode is 1.

FIG. 106 is a flowchart of a prediction mode selection process (S3582)shown in FIG. 105 .

First, the three-dimensional data encoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to be encoded to prediction modes 1to N sequentially from a three-dimensional point closer to the targetthree-dimensional point (S35821). For example, the three-dimensionaldata encoding device generates N+1 prediction modes. When N+1 is largerthan total number M of prediction modes (NumPredMode) appended to thebitstream, the three-dimensional data encoding device may generate up toM prediction modes.

Then, the three-dimensional data encoding device calculates cost of eachprediction mode, and selects a prediction mode with minimum cost(S35822).

FIG. 107 is a flowchart of a specific example of a prediction modeselection process (S35822) shown in FIG. 106 .

First, the three-dimensional data encoding device sets i=0 and mincost=∞(S358221).

Then, the three-dimensional data encoding device calculates cost[i] ofi-th prediction mode PredMode[i] (S358222).

Then, the three-dimensional data encoding device determines whether ornot cost[i]<mincost is satisfied (S358223).

Then, when determining that cost[i]<mincost is satisfied (Yes inS358223), the three-dimensional data encoding device setsmincost=cost[i], and sets the prediction mode to PredMode[i] (S358224).

Following step S358224 or when determining that cost[i]<mincost is notsatisfied (No in S358223), the three-dimensional data encoding devicesets i=i+1 (S358225).

Then, the three-dimensional data encoding device determines whether ornot i<the number of prediction modes is satisfied (S358226).

When determining that i<the number of prediction modes is not satisfied(No in S358226), the three-dimensional data encoding device finishes theselection process. When determining that i<the number of predictionmodes is satisfied (Yes in S358226), the three-dimensional data encodingdevice returns the process to step S358222.

When the number of three-dimensional points in the vicinity of thetarget three-dimensional point to be encoded is N, the three-dimensionaldata encoding device generates N+1 prediction modes. When N+1 is largerthan total number M of prediction modes (NumPredMode) appended to thebitstream, the three-dimensional data encoding device may generate up toM prediction modes.

FIG. 108 is a flowchart of another example of the calculation process ofa prediction mode and a quantized value of P (S355211) shown in FIG. 97.

First, the three-dimensional data decoding device decodes the predictionmode from the bitstream (S3552111).

As described above, the three-dimensional data decoding device mayarithmetic-decode prediction mode PredMode in accordance with totalnumber M of prediction modes obtained by decoding the header.

When total number M of prediction modes is 1, the three-dimensional datadecoding device may estimate PredMode as 0 without decoding PredMode.

Then, the three-dimensional data decoding device arithmetic-decodes ann-bit code (S3552112).

Then, the three-dimensional data decoding device determines whether ornot a value of the n-bit code==R_TH[LoDN] is satisfied (S3552113).

When determining that the value of the n-bit code==R_TH[LoDN] issatisfied (Yes in S3552113), the three-dimensional data decoding devicearithmetic-decodes a remaining code (S3552114).

Then, the three-dimensional data decoding device calculates a value ofthe remaining code with reference to a table for an Exponential-Golombcode (S3552115).

Then, the three-dimensional data decoding device sets (R_TH[LoDN]+valueof remaining code) as a prediction residual (S3552116).

Then, the three-dimensional data decoding device transforms the decodedprediction residual from an unsigned integer value to a signed integervalue (S3552117).

When determining that the value of the n-bit code==R_TH[LoDN] is notsatisfied (No in S3552113), the three-dimensional data decoding devicedecodes the prediction mode from the bitstream (S3552118), and performsstep S3552117.

Embodiment 6

In the present embodiment, a method of calculating a predicted value ofattribute information of a three-dimensional point is described.

In order to encode or decode attribute information of athree-dimensional point cloud, the attribute information is encoded ordecoded using a predicted value of the attribute information.Calculating an optimum predicted value (optimum prediction) leads toimproving the coding efficiency.

Note that the optimum prediction need not necessarily be minimizing abitstream or adopting the highest compression ratio. Moreover, beingoptimum depends on user's definition, and refers to maintaining thequality rather than fixed compression in some cases.

For example, as illustrated in FIG. 109 , for encoding attributeinformation of a current three-dimensional point to be encoded,attribute information of prediction candidate 2 and attributeinformation of prediction candidate 4 can be used. Prediction candidate2 has a similar color and a slightly different pattern, and predictioncandidate 4 has a different color and the same pattern. Note that FIG.109 is a diagram illustrating an example of the currentthree-dimensional point (first three-dimensional point) to be encodedand one or more three-dimensional points (second three-dimensionalpoints) in the vicinity (neighborhood) of the first three-dimensionalpoint.

In the case where an encoding process of attribute information isperformed with more emphasis on the color than the pattern, theattribute information of prediction candidate 2 is used as illustratedin FIG. 110 . If prediction candidate 2 is used for encoding theattribute information, costs for encoding the color can be reduced whilethe pattern is encoded. In order to realize a higher compression ratio,there is a technique of calculating costs for encoding eachthree-dimensional point; encoding the attribute information throughprediction using the attribute information of one point or some points;and thus selecting an optimum method for improving the codingefficiency. Note that FIG. 110 is a diagram illustrating an example ofthe current three-dimensional point (first three-dimensional point) tobe encoded and the one or more three-dimensional points (secondthree-dimensional points) in the vicinity (neighborhood) of the firstthree-dimensional point.

In the case where the encoding process of the attribute information isperformed with more emphasis on the pattern than the color, theattribute information of prediction candidate 4 is used as illustratedin FIG. 111 . If prediction candidate 4 is used for encoding theattribute information, costs for encoding the pattern can be reducedwhile the color is encoded. Moreover, even if the compressionperformance decreases depending on user's preference and settings, theprediction candidate can be also changed on the basis of a particularstandard on which the user puts emphasis.

In order to control algorithm better, an optimum candidate is selectedas a prediction factor. To achieve this, some additional parameters arenecessary. The introduced parameters include an offset and λ. The offsetis a parameter for directly changing a threshold value according to agiven method. λ is a parameter for changing the slope of a functiondepending on other parameters of the algorithm. The cost calculation ina predictor can be easily changed by combining the offset and λ.

This makes the following possible. The cost calculation in the case ofusing a different predictor can be more efficiently performed, and moreaccurate predictor algorithm can be constructed. Characteristics of apredictor can be changed so as to suit user's utilization scene and adata set. Even if the offset or the lambda value is changed, the basicalgorithm remains the same, and hence the same performance andcharacteristics can be maintained.

For example, if the offset is changed, a graph in FIG. 112 illustratinga relation between a cost and a bit count can be changed to a graphillustrated in FIG. 113 . Moreover, if λ is changed, a graph illustratedin FIG. 112 can be changed to a graph illustrated in FIG. 114 .

A prediction mode at the time of encoding may be selected by RDoptimization. Note that the prediction mode is information indicating amethod of calculating (how to calculate) a predicted value. For example,it is conceivable to: calculate cost cost(P) in the case of selectinggiven prediction mode P; and select prediction mode P in which cost(P)is the smallest. Cost cost(P) may be calculated according to Equation Y1using, for example: predicted residual residual(P) in the case of usinga predicted value in prediction mode P; and bit count bit(P), anadjustment parameter λ value, or offset value offset(P) necessary toencode prediction mode P.

cost(P)=abs(residual(P))+λ×bit(P)−offset(P)  (Equation Y1)

abs(x) indicates an absolute value of x.

A square value of x may be used instead of abs(x).

The use of Equation Y1 given above makes it possible to select aprediction mode for which the balance between the size of a predictionresidual and the bit count or the offset value necessary to encode theprediction mode is taken into consideration. Note that the value ofadjustment parameter λ may be set to a different value in accordancewith the value of a quantization scale. For example, in the case wherethe quantization scale is small (at a high bit rate), a prediction modein which predicted residual residual(P) is small may be selected bymaking the λ value smaller, whereby the prediction precision may beimproved as much as possible. In the case where the quantization scaleis large (at a low bit rate), an appropriate prediction mode may beselected while bit count bit(P) necessary to encode prediction mode P istaken into consideration by making the λ value larger.

Note that the case where the quantization scale is small means, forexample, the case where the quantization scale is smaller than a firstquantization scale. The case where the quantization scale is largemeans, for example, the case where the quantization scale is larger thana second quantization scale that is larger than or equal to the firstquantization scale. Moreover, as the quantization scale becomes smaller,the λ value may be set to a smaller value.

Moreover, offset value offset(P) may be controlled to a value based ongiven prediction mode P. For example, the following control may beperformed: offset=30 is set for prediction mode 0 in which predictionmode information indicates “0”; offset=0 is set for prediction modesother than prediction mode 0; and the cost value in prediction mode 0 isthus made smaller, whereby the selection rate of prediction mode 0 israised. In this way, if the selection rate of the given prediction modeis improved by controlling the offset value, fluctuations in theselected prediction mode can be suppressed, and the code amount at thetime of entropically encoding the prediction mode information can bereduced.

Moreover, the selection rate of prediction mode 0 is improved bycontrolling the offset value, and, as illustrated in FIG. 115 or FIG.116 , the code amount of a prediction mode in the case where binarizeddata whose bit length is short is assigned to prediction mode 0 is thusreduced, whereby the coding efficiency may be improved. Note that theoffset value may be set to a value based on the value of thequantization scale. For example, in the case where the value of thequantization scale is small (at a high bit rate), a prediction mode inwhich predicted residual residual(P) is small may be selected by settingthe offset value to a small value, whereby the prediction precision maybe improved as much as possible. Moreover, in the case where thequantization scale is large (at a low bit rate), the selection rate ofgiven prediction mode P may be improved by setting the offset value to alarge value, whereby fluctuations in selection of the prediction modemay be suppressed and the bit count for entropically encoding theprediction mode information may be reduced.

Note that the cost of prediction mode P is increased by setting theoffset to a negative value, whereby the selection rate of predictionmode P may be decreased and the selection rates other than that ofprediction mode P may be improved. In this manner, fluctuations inselection of the prediction mode may be suppressed, and the bit countfor entropically encoding the prediction mode information may bereduced.

Note that switching may be made to at least one of the λ value and theoffset value in accordance with attribute information to be encoded. Forexample, in the case where the attribute information is informationincluding three elements and where one prediction mode is used for threeresidual values (prediction residuals), a prediction mode in which threepredicted residuals residual(P) are small may be selected by setting atleast one of the λ value and the offset value in prediction mode 0 to asmall value, whereby the prediction precision may be improved as much aspossible. In this manner, the encoding efficiency may be improved. Notethat examples of the attribute information that is information includingthree elements include: color information having three elements of RGB;and a normal vector having x-, y-, and z-directional vectors.

Moreover, in the case where the attribute information is informationincluding one element and where one prediction mode is used for oneresidual value, the selection rate of given prediction mode 0 may beimproved by setting at least one of the λ value and the offset value inprediction mode 0 to a large value, whereby fluctuations in selection ofthe prediction mode may be suppressed and the bit count for entropicallyencoding the prediction mode information may be reduced. In this manner,the coding efficiency may be improved. Note that the attributeinformation that is information including one element is, for example, areflectance.

Predicted residual residual(P) is calculated by subtracting thepredicted value in prediction mode P from an attribute value of thecurrent three-dimensional point to be encoded. Note that, instead ofpredicted residual residual(P) at the time of cost calculation, it isacceptable to: quantize and inversely quantize predicted residualresidual(P); add the result to the predicted value to obtain a decodedvalue; and reflect, in the cost value, a difference (encoding error)between an attribute value of the original three-dimensional point andthe decoded value in the case of using prediction mode P. This makes itpossible to select a prediction mode in which the encoding error issmall.

For example, in the case where the prediction mode is binarized andencoded, a bit count after the binarization may be used as bit countbit(P) necessary to encode prediction mode P. For example, in the casewhere number M of prediction modes=5, a prediction mode value may bebinarized, the prediction mode value indicating the prediction mode bymeans of a truncated unary code with the maximum value being 5 usingnumber M of prediction modes, as illustrated in FIG. 115 . In this case,as respective bit counts bit(P) necessary to encode the followingprediction mode values, used are: 1 bit in the case where the predictionmode value is “0”; 2 bits in the case where the prediction mode value is“1”; 3 bits in the case where the prediction mode value is “2”; and 4bits in the case where the prediction mode values are “3” and “4”. Ifthe truncated unary code is used, the bit count becomes lower as thevalue in the prediction mode becomes smaller. Therefore, the code amountof a prediction mode value can be reduced, the prediction mode valueindicating a prediction mode for calculating a predicted value that ismore likely to be selected, for example, more likely to make cost(P) thesmallest. Examples of such a predicted value include: an average valuethat is calculated as a predicted value in the case where the predictionmode value is “0”; and attribute information of a three-dimensionalpoint that is calculated as a predicted value in the case where theprediction mode value is “1,” that is, attribute information of athree-dimensional point near the current three-dimensional point to beencoded.

Moreover, in the case where the maximum value of the number ofprediction modes is not determined, as illustrated in FIG. 116 , theprediction mode value indicating the prediction mode may be binarized bymeans of a unary code. Moreover, in the case where the respectiveoccurrence probabilities of the prediction modes are close to oneanother, as illustrated in FIG. 117 , the prediction mode valueindicating each prediction mode may be binarized by means of a fixedcode, and the code amount may thus be reduced.

Note that, as bit count bit(P) necessary to encode the prediction modevalue indicating prediction mode P, it is acceptable to arithmeticallyencode binarized data of the prediction mode value indicating predictionmode P and use the code amount after this arithmetic encoding, as thevalue of bit(P). In this manner, the cost can be calculated using moreaccurate necessary bit count bit(P), and hence a more appropriateprediction mode can be selected.

Note that FIG. 115 is a diagram illustrating a first example of abinarization table in the case where the prediction mode value isbinarized and encoded. Specifically, the first example is an example inwhich, in the case where number M of prediction modes=5, the predictionmode value is binarized by means of a truncated unary code.

Moreover, FIG. 116 is a diagram illustrating a second example of thebinarization table in the case where the prediction mode value isbinarized and encoded. Specifically, the second example is an example inwhich, in the case where number M of prediction modes=5, the predictionmode value is binarized by means of a unary code.

Moreover, FIG. 117 is a diagram illustrating a third example of thebinarization table in the case where the prediction mode value isbinarized and encoded. Specifically, the third example is an example inwhich, in the case where number M of prediction modes=5, the predictionmode value is binarized by means of a fixed code.

Note that, in the present embodiment, description is given of theexample in which: at the time of cost calculation, the offset value ingiven prediction mode P is set to be larger than those in otherprediction modes; and the selection rate of prediction mode P is thusimproved, but the present disclosure is not necessarily limited to this.It is acceptable to adopt any method as long as the method enablespreferentially selecting prediction mode P. For example, in order toimprove the selection rate of prediction mode 0 as an example ofprediction mode P, the selection rate of prediction mode 0 may beimproved by multiplying the cost value in prediction mode 0 by ½. Inthis way, because the selection rate of prediction mode 0 is improved,fluctuations in the selected prediction mode can be suppressed, and thecode amount at the time of entropically encoding the prediction modeinformation can be reduced.

Note that, in the present embodiment, description is given of theexample in which: the selection rate of prediction mode 0 is improved,for example, prediction mode 0 is set to be more easily selected, bycontrolling at least one value of the λ value and the offset value;fluctuations in the value in the selected prediction mode are thussuppressed; and the code amount is thus reduced, but the presentdisclosure is not necessarily limited to this. For example, athree-dimensional data encoding device may make switching between:selecting a prediction mode from among a plurality of prediction modes;and using a fixed prediction mode (for example, prediction mode 0), inaccordance with the type of attribute information to be encoded.

Specifically, the three-dimensional data encoding device may calculate apredicted value using one prediction mode among two or more predictionmodes, when the type of attribute information is attribute information(first attribute information) including elements the total number ofwhich is greater than a predetermined threshold value. The firstattribute information may be information including three elements, andexamples of the first attribute information include: color informationindicating color of a three-dimensional point and having three elementsof RGB; and a normal vector having x-, y-, and z-directional vectors ofa three-dimensional point. In the case where one prediction mode is usedto calculate a predicted value for calculating the prediction residualof each element in the attribute information including three elements, aprediction mode in which three predicted residuals residual(P) are smallmay be selected from a plurality of prediction candidates, whereby theencoding efficiency may be improved.

Moreover, the three-dimensional data encoding device may calculate apredicted value using one fixed prediction mode, when the type ofattribute information is attribute information (second attributeinformation) including elements the total number of which is smallerthan or equal to the predetermined threshold value. The one fixedprediction mode may be one prediction mode among two or more predictionmodes, and may be, for example, prediction mode 0. The second attributeinformation may be information including one element, and examples ofthe second attribute information include reflectance informationindicating a reflectance of a three-dimensional point. In the case whereone prediction mode is used to calculate a predicted value forcalculating one prediction residual in the attribute informationincluding one element, the prediction mode may be fixed to 0.

Note that the predetermined threshold value may be 1. That is, inaccordance with whether or not the attribute information includes aplurality of elements, the three-dimensional data encoding device maydetermine whether to calculate the predicted value in one predictionmode among two or more prediction modes or to calculate the predictedvalue in the one fixed prediction mode.

In this way, in the case where the fixed prediction mode is used as theprediction mode for calculating the predicted value, thethree-dimensional data encoding device need not add the prediction modeinformation indicating the fixed prediction mode, to the bitstream. Thatis, in the case where the prediction mode for calculating the predictedvalue is the one fixed prediction mode, the bitstream need not includethe prediction mode information (index value) indicating the predictionmode. This is because a three-dimensional data decoding device candetermine that the fixed prediction mode is used as the prediction modein the case where the bitstream does not include the prediction modeinformation. In this case, the prediction mode information is not addedto the bitstream, and hence the code amount can be reduced.

In this way, on the basis of whether or not the prediction modeinformation is added to the bitstream, the three-dimensional dataencoding device can indicate whether or not the one fixed predictionmode is used as the prediction mode. That is, the bitstream indicateswhether or not the prediction mode is the one fixed prediction mode.

Note that the three-dimensional data encoding device may add at leastone value of the λ value and the offset value at the time of costcalculation, to the bitstream. This enables the three-dimensional datadecoding device to know, by decoding the bitstream, at least one valueof the A value and the offset value used by the three-dimensional dataencoding device and to know according to what method the prediction modeis selected to generate the bitstream.

Moreover, the three-dimensional data encoding device may calculate, asthe predicted value of the attribute information of the firstthree-dimensional point, the average value of the one or more items ofattribute information of the one or more second three-dimensional pointsin the vicinity (neighborhood) of the first three-dimensional point,when the value indicated by the prediction mode information is 0, thatis, in the case of prediction mode 0. That is, the predicted value ofthe attribute information of the first three-dimensional point in thecase of prediction mode 0 is the average value of the one or more itemsof attribute information of the one or more second three-dimensionalpoints.

FIG. 118 is a flowchart illustrating a method of determining whether ornot to fix the prediction mode by the three-dimensional data encodingdevice.

The three-dimensional data encoding device determines whether or not thetype of attribute information of a current three-dimensional point to beencoded is information including a plurality of elements (S13201). Thethree-dimensional data encoding device determines whether the type ofthe attribute information of the current three-dimensional point to beencoded is the first attribute information including elements the totalnumber of which is greater than the predetermined threshold value or thesecond attribute information including elements the total number ofwhich is smaller than or equal to the predetermined threshold value.

In the case where the three-dimensional data encoding device determinesthat the type of the attribute information of the currentthree-dimensional point to be encoded is the information including theplurality of elements (Yes in S13201), the three-dimensional dataencoding device selects one prediction mode from the plurality ofprediction modes to perform encoding (S13202). That is, in the casewhere the three-dimensional data encoding device determines that theattribute information of the current three-dimensional point to beencoded is the first attribute information, the three-dimensional dataencoding device calculates a predicted value using one prediction modeamong two or more prediction modes, and encodes the attributeinformation of the current three-dimensional point to be encoded, usingthis predicted value. In this case, the three-dimensional data encodingdevice adds the prediction mode information indicating the selected oneprediction mode, to the bitstream.

In the case where the three-dimensional data encoding device determinesthat the type of the attribute information of the currentthree-dimensional point to be encoded is different from the informationincluding the plurality of elements (No in S13201), thethree-dimensional data encoding device performs encoding usingprediction mode 0 (S13203). That is, in the case where thethree-dimensional data encoding device determines that the attributeinformation of the current three-dimensional point to be encoded is thesecond attribute information, the three-dimensional data encoding devicecalculates a predicted value using one fixed prediction mode, andencodes the attribute information of the current three-dimensional pointto be encoded, using this predicted value. In this case, thethree-dimensional data encoding device may add the prediction modeinformation indicating the one fixed prediction mode (for example,prediction mode 0), to the bitstream. Moreover, the three-dimensionaldata encoding device need not add the prediction mode information to thebitstream.

FIG. 119 is a flowchart illustrating a decoding method by thethree-dimensional data decoding device.

The three-dimensional data decoding device determines whether or not thetype of attribute information of a current three-dimensional point to bedecoded is information including a plurality of elements (S13211). Thethree-dimensional data decoding device determines whether the type ofthe attribute information of the current three-dimensional point to bedecoded is the first attribute information including elements the totalnumber of which is greater than a predetermined threshold value or thesecond attribute information including elements the total number ofwhich is smaller than or equal to the predetermined threshold value.Note that, for example, the predetermined threshold value is 1.

In the case where the three-dimensional data decoding device determinesthat the type of the attribute information of the currentthree-dimensional point to be decoded is the information including theplurality of elements (Yes in S13211), the three-dimensional datadecoding device performs decoding using the prediction mode indicated bythe prediction mode information included in the bitstream (S13212). Thatis, in the case where the three-dimensional data decoding devicedetermines that the attribute information of the currentthree-dimensional point to be decoded is the first attributeinformation, the three-dimensional data decoding device calculates apredicted value using the prediction mode indicated by the predictionmode information included in the bitstream, and decodes the attributeinformation of the current three-dimensional point to be decoded, usingthis predicted value.

In the case where the three-dimensional data decoding device determinesthat the type of the attribute information of the currentthree-dimensional point to be decoded is different from the informationincluding the plurality of elements (No in S13211), thethree-dimensional data decoding device performs decoding usingprediction mode 0 (S13213). That is, in the case where thethree-dimensional data decoding device determines that the attributeinformation of the current three-dimensional point to be decoded is thesecond attribute information, the three-dimensional data decoding devicecalculates a predicted value using prediction mode 0, and decodes theattribute information of the current three-dimensional point to bedecoded, using this predicted value.

Note that the determining in Step S13211 may be performed in accordancewith whether or not the bitstream indicates that the used predictionmode is the one fixed prediction mode. In the case where the bitstreamindicates that the used prediction mode is different from the one fixedprediction mode, for example, in the case where the bitstream includesthe prediction mode information, the three-dimensional data decodingdevice may calculate the predicted value in the prediction modeindicated by the prediction mode information. Moreover, in the casewhere the bitstream indicates that the used prediction mode is the onefixed prediction mode, for example, in the case where the bitstream doesnot include the prediction mode information, the three-dimensional datadecoding device may calculate the predicted value in the one fixedprediction mode.

Moreover, the bitstream may indicate whether or not the used predictionmode is the one fixed prediction mode, by means of NumPredMode (numberof prediction modes) indicating the number of prediction modes. Forexample, in the case where NumPredMode is greater than 1, it may beindicated that the used prediction mode is different from the one fixedprediction mode. In the case where NumPredMode is 1, it may be indicatedthat the used prediction mode is the one fixed prediction mode. That is,in the case where NumPredMode is greater than 1, the three-dimensionaldata decoding device may execute Step S13212. In the case whereNumPredMode is 1, the three-dimensional data decoding device may executeStep S13213.

As described above, the three-dimensional data encoding device accordingto the present embodiment performs processing illustrated in FIG. 120 .The three-dimensional data encoding device calculates the predictedvalue of the attribute information of the first three-dimensional pointin the prediction mode for calculating the predicted value, using theone or more items of attribute information of the one or more secondthree-dimensional points in the vicinity of the first three-dimensionalpoint (S13221). The three-dimensional data encoding device calculates aprediction residual that is a difference between the attributeinformation of the first three-dimensional point and the predicted valuecalculated (S13222). The three-dimensional data encoding devicegenerates the bitstream including the prediction residual and theprediction mode information indicating the prediction mode (S13223). Theprediction mode is one prediction mode among two or more predictionmodes when the type of the attribute information of the firstthree-dimensional point is the first attribute information includingelements the total number of which is greater than the predeterminedthreshold value. The prediction mode is one fixed prediction mode whenthe type of the attribute information of the first three-dimensionalpoint is the second attribute information including elements the totalnumber of which is smaller than or equal to the predetermined thresholdvalue.

Accordingly, attribute information including a large number of elementscan be encoded using one prediction mode selected from two or moreprediction modes. Hence, the overhead can be reduced, and the codingefficiency of the attribute information can be improved.

For example, the bitstream indicates whether or not the prediction modeis the one fixed prediction mode.

For example, the predetermined threshold value is 1.

For example, the bitstream does not include the prediction modeinformation when the one prediction mode is the one fixed predictionmode.

For example, the predicted value is the average value of the one or moreitems of attribute information of the one or more secondthree-dimensional points, when the value indicated by the predictionmode information is 0.

For example, the first attribute information is color informationindicating color of a three-dimensional point, and the second attributeinformation is information indicating a reflectance of athree-dimensional point.

For example, the three-dimensional data encoding device includes aprocessor and a memory, and the processor performs the above-mentionedprocessing using the memory.

Moreover, the three-dimensional data decoding device according to thepresent embodiment performs processing illustrated in FIG. 121 . Thethree-dimensional data decoding device obtains the prediction residualand the prediction mode information indicating the prediction mode forthe first three-dimensional point among the plurality ofthree-dimensional points, by obtaining the bitstream (S13231). Thethree-dimensional data decoding device calculates the predicted value inthe prediction mode indicated by the prediction mode information(S13232). The three-dimensional data decoding device calculates theattribute information of the first three-dimensional point by adding thepredicted value to the prediction residual (S13233). The prediction modeis one prediction mode among two or more prediction modes when the typeof the attribute information of the first three-dimensional point is thefirst attribute information including elements the total number of whichis greater than the predetermined threshold value. The prediction modeis one fixed prediction mode when the type of the attribute informationof the first three-dimensional point is the second attribute informationincluding elements the total number of which is smaller than or equal tothe predetermined threshold value.

Accordingly, the attribute information that has been encoded using thepredicted value in the prediction mode indicated by the bitstream can beappropriately decoded.

For example, the bitstream indicates whether or not the one predictionmode is the one fixed prediction mode. The three-dimensional datadecoding device performs the calculating of the predicted value in theprediction mode indicated by the prediction mode information, when thebitstream indicates that the one prediction mode is different from theone fixed prediction mode. The three-dimensional data decoding deviceperforms the calculating of the predicted value in the one fixedprediction mode, when the bitstream indicates that the one predictionmode is the one fixed prediction mode.

For example, the predetermined threshold value is 1.

For example, the three-dimensional data decoding device performs thecalculating of the predicted value in the one fixed prediction mode whenthe bitstream does not include the prediction mode information.

For example, the predicted value is the average value of the one or moreitems of attribute information of the one or more secondthree-dimensional points, when the value indicated by the predictionmode information is 0.

For example, the first attribute information is color informationindicating color of a three-dimensional point, and the second attributeinformation is information indicating a reflectance of athree-dimensional point.

For example, the three-dimensional data decoding device includes aprocessor and a memory, and the processor performs the above-mentionedprocessing using the memory.

Embodiment 7

The following describes the structure of three-dimensional data creationdevice 810 according to the present embodiment. FIG. 122 is a blockdiagram of an exemplary structure of three-dimensional data creationdevice 810 according to the present embodiment. Such three-dimensionaldata creation device 810 is equipped, for example, in a vehicle.Three-dimensional data creation device 810 transmits and receivesthree-dimensional data to and from an external cloud-based trafficmonitoring system, a preceding vehicle, or a following vehicle, andcreates and stores three-dimensional data.

Three-dimensional data creation device 810 includes data receiver 811,communication unit 812, reception controller 813, format converter 814,a plurality of sensors 815, three-dimensional data creator 816,three-dimensional data synthesizer 817, three-dimensional data storage818, communication unit 819, transmission controller 820, formatconverter 821, and data transmitter 822.

Data receiver 811 receives three-dimensional data 831 from a cloud-basedtraffic monitoring system or a preceding vehicle. Three-dimensional data831 includes, for example, information on a region undetectable bysensors 815 of the own vehicle, such as a point cloud, visible lightvideo, depth information, sensor position information, and speedinformation.

Communication unit 812 communicates with the cloud-based trafficmonitoring system or the preceding vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the preceding vehicle.

Reception controller 813 exchanges information, such as information onsupported formats, with a communications partner via communication unit812 to establish communication with the communications partner.

Format converter 814 applies format conversion, etc. onthree-dimensional data 831 received by data receiver 811 to generatethree-dimensional data 832. Format converter 814 also decompresses ordecodes three-dimensional data 831 when three-dimensional data 831 iscompressed or encoded.

A plurality of sensors 815 are a group of sensors, such as visible lightcameras and infrared cameras, that obtain information on the outside ofthe vehicle and generate sensor information 833. Sensor information 833is, for example, three-dimensional data such as a point cloud (pointgroup data), when sensors 815 are laser sensors such as LiDARs. Notethat a single sensor may serve as a plurality of sensors 815.

Three-dimensional data creator 816 generates three-dimensional data 834from sensor information 833. Three-dimensional data 834 includes, forexample, information such as a point cloud, visible light video, depthinformation, sensor position information, and speed information.

Three-dimensional data synthesizer 817 synthesizes three-dimensionaldata 834 created on the basis of sensor information 833 of the ownvehicle with three-dimensional data 832 created by the cloud-basedtraffic monitoring system or the preceding vehicle, etc., therebyforming three-dimensional data 835 of a space that includes the spaceahead of the preceding vehicle undetectable by sensors 815 of the ownvehicle.

Three-dimensional data storage 818 stores generated three-dimensionaldata 835, etc.

Communication unit 819 communicates with the cloud-based trafficmonitoring system or the following vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the following vehicle.

Transmission controller 820 exchanges information such as information onsupported formats with a communications partner via communication unit819 to establish communication with the communications partner.Transmission controller 820 also determines a transmission region, whichis a space of the three-dimensional data to be transmitted, on the basisof three-dimensional data formation information on three-dimensionaldata 832 generated by three-dimensional data synthesizer 817 and thedata transmission request from the communications partner.

More specifically, transmission controller 820 determines a transmissionregion that includes the space ahead of the own vehicle undetectable bya sensor of the following vehicle, in response to the data transmissionrequest from the cloud-based traffic monitoring system or the followingvehicle. Transmission controller 820 judges, for example, whether aspace is transmittable or whether the already transmitted space includesan update, on the basis of the three-dimensional data formationinformation to determine a transmission region. For example,transmission controller 820 determines, as a transmission region, aregion that is: a region specified by the data transmission request; anda region, corresponding three-dimensional data 835 of which is present.Transmission controller 820 then notifies format converter 821 of theformat supported by the communications partner and the transmissionregion.

Of three-dimensional data 835 stored in three-dimensional data storage818, format converter 821 converts three-dimensional data 836 of thetransmission region into the format supported by the receiver end togenerate three-dimensional data 837. Note that format converter 821 maycompress or encode three-dimensional data 837 to reduce the data amount.

Data transmitter 822 transmits three-dimensional data 837 to thecloud-based traffic monitoring system or the following vehicle. Suchthree-dimensional data 837 includes, for example, information on a blindspot, which is a region hidden from view of the following vehicle, suchas a point cloud ahead of the own vehicle, visible light video, depthinformation, and sensor position information.

Note that an example has been described in which format converter 814and format converter 821 perform format conversion, etc., but formatconversion may not be performed.

With the above structure, three-dimensional data creation device 810obtains, from an external device, three-dimensional data 831 of a regionundetectable by sensors 815 of the own vehicle, and synthesizesthree-dimensional data 831 with three-dimensional data 834 that is basedon sensor information 833 detected by sensors 815 of the own vehicle,thereby generating three-dimensional data 835. Three-dimensional datacreation device 810 is thus capable of generating three-dimensional dataof a range undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 is also capable oftransmitting, to the cloud-based traffic monitoring system or thefollowing vehicle, etc., three-dimensional data of a space that includesthe space ahead of the own vehicle undetectable by a sensor of thefollowing vehicle, in response to the data transmission request from thecloud-based traffic monitoring system or the following vehicle.

The following describes the steps performed by three-dimensional datacreation device 810 of transmitting three-dimensional data to afollowing vehicle. FIG. 123 is a flowchart showing exemplary stepsperformed by three-dimensional data creation device 810 of transmittingthree-dimensional data to a cloud-based traffic monitoring system or afollowing vehicle.

First, three-dimensional data creation device 810 generates and updatesthree-dimensional data 835 of a space that includes space on the roadahead of the own vehicle (S801). More specifically, three-dimensionaldata creation device 810 synthesizes three-dimensional data 834 createdon the basis of sensor information 833 of the own vehicle withthree-dimensional data 831 created by the cloud-based traffic monitoringsystem or the preceding vehicle, etc., for example, thereby formingthree-dimensional data 835 of a space that also includes the space aheadof the preceding vehicle undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 then judges whether anychange has occurred in three-dimensional data 835 of the space includedin the space already transmitted (S802).

When a change has occurred in three-dimensional data 835 of the spaceincluded in the space already transmitted due to, for example, a vehicleor a person entering such space from outside (Yes in S802),three-dimensional data creation device 810 transmits, to the cloud-basedtraffic monitoring system or the following vehicle, thethree-dimensional data that includes three-dimensional data 835 of thespace in which the change has occurred (S803).

Three-dimensional data creation device 810 may transmitthree-dimensional data in which a change has occurred, at the sametiming of transmitting three-dimensional data that is transmitted at apredetermined time interval, or may transmit three-dimensional data inwhich a change has occurred soon after the detection of such change.Stated differently, three-dimensional data creation device 810 mayprioritize the transmission of three-dimensional data of the space inwhich a change has occurred to the transmission of three-dimensionaldata that is transmitted at a predetermined time interval.

Also, three-dimensional data creation device 810 may transmit, asthree-dimensional data of a space in which a change has occurred, thewhole three-dimensional data of the space in which such change hasoccurred, or may transmit only a difference in the three-dimensionaldata (e.g., information on three-dimensional points that have appearedor vanished, or information on the displacement of three-dimensionalpoints).

Three-dimensional data creation device 810 may also transmit, to thefollowing vehicle, meta-data on a risk avoidance behavior of the ownvehicle such as hard breaking warning, before transmittingthree-dimensional data of the space in which a change has occurred. Thisenables the following vehicle to recognize at an early stage that thepreceding vehicle is to perform hard braking, etc., and thus to startperforming a risk avoidance behavior at an early stage such as speedreduction.

When no change has occurred in three-dimensional data 835 of the spaceincluded in the space already transmitted (No in S802), or after stepS803, three-dimensional data creation device 810 transmits, to thecloud-based traffic monitoring system or the following vehicle,three-dimensional data of the space included in the space having apredetermined shape and located ahead of the own vehicle by distance L(S804).

The processes of step S801 through step S804 are repeated, for exampleat a predetermined time interval.

When three-dimensional data 835 of the current space to be transmittedincludes no difference from the three-dimensional map, three-dimensionaldata creation device 810 may not transmit three-dimensional data 837 ofthe space.

In the present embodiment, a client device transmits sensor informationobtained through a sensor to a server or another client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 124 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LiDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 125 is a block diagram showing an example structure of clientdevice 902. Client device 902 receives the three-dimensional map formedby a point cloud and the like from server 901, and estimates aself-location of client device 902 using the three-dimensional mapcreated based on the sensor information of client device 902. Clientdevice 902 transmits the obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g., transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 1015 are a group of sensors, such as LiDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point group data)when sensors 1015 are laser sensors such as LiDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LiDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LiDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 126 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LiDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g., transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when received sensor information 1037is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LiDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG.127 is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 128 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 129 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 130 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

The following describes variations of the present embodiment.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LiDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 131 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 132 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmitsobtained sensor information 1033 to server 901 or another client device902.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1033 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another clientdevice 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using received sensor information1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses received sensor information 1037, andcreates three-dimensional data 1134 using sensor information 1132 thathas been decoded or decompressed. This enables server 901 to reduce theamount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

The following will describe a variation of the present embodiment. FIG.133 is a diagram illustrating a configuration of a system according tothe present embodiment. The system illustrated in FIG. 133 includesserver 2001, client device 2002A, and client device 2002B.

Client device 2002A and client device 2002B are each provided in amobile object such as a vehicle, and transmit sensor information toserver 2001. Server 2001 transmits a three-dimensional map (a pointcloud) to client device 2002A and client device 2002B.

Client device 2002A includes sensor information obtainer 2011, storage2012, and data transmission possibility determiner 2013. It should benoted that client device 2002B has the same configuration. Additionally,when client device 2002A and client device 2002B are not particularlydistinguished below, client device 2002A and client device 2002B arealso referred to as client device 2002.

FIG. 134 is a flowchart illustrating operation of client device 2002according to the present embodiment.

Sensor information obtainer 2011 obtains a variety of sensor informationusing sensors (a group of sensors) provided in a mobile object. In otherwords, sensor information obtainer 2011 obtains sensor informationobtained by the sensors (the group of sensors) provided in the mobileobject and indicating a surrounding state of the mobile object. Sensorinformation obtainer 2011 also stores the obtained sensor informationinto storage 2012. This sensor information includes at least one ofinformation obtained by LiDAR, a visible light image, an infrared image,or a depth image. Additionally, the sensor information may include atleast one of sensor position information, speed information, obtainmenttime information, or obtainment location information. Sensor positioninformation indicates a position of a sensor that has obtained sensorinformation. Speed information indicates a speed of the mobile objectwhen a sensor obtained sensor information. Obtainment time informationindicates a time when a sensor obtained sensor information. Obtainmentlocation information indicates a position of the mobile object or asensor when the sensor obtained sensor information.

Next, data transmission possibility determiner 2013 determines whetherthe mobile object (client device 2002) is in an environment in which themobile object can transmit sensor information to server 2001 (S2002).For example, data transmission possibility determiner 2013 may specify alocation and a time at which client device 2002 is present using GPSinformation etc., and may determine whether data can be transmitted.Additionally, data transmission possibility determiner 2013 maydetermine whether data can be transmitted, depending on whether it ispossible to connect to a specific access point.

When client device 2002 determines that the mobile object is in theenvironment in which the mobile object can transmit the sensorinformation to server 2001 (YES in S2002), client device 2002 transmitsthe sensor information to server 2001 (S2003). In other words, whenclient device 2002 becomes capable of transmitting sensor information toserver 2001, client device 2002 transmits the sensor information held byclient device 2002 to server 2001. For example, an access point thatenables high-speed communication using millimeter waves is provided inan intersection or the like. When client device 2002 enters theintersection, client device 2002 transmits the sensor information heldby client device 2002 to server 2001 at high speed using themillimeter-wave communication.

Next, client device 2002 deletes from storage 2012 the sensorinformation that has been transmitted to server 2001 (S2004). It shouldbe noted that when sensor information that has not been transmitted toserver 2001 meets predetermined conditions, client device 2002 maydelete the sensor information. For example, when an obtainment time ofsensor information held by client device 2002 precedes a current time bya certain time, client device 2002 may delete the sensor informationfrom storage 2012. In other words, when a difference between the currenttime and a time when a sensor obtained sensor information exceeds apredetermined time, client device 2002 may delete the sensor informationfrom storage 2012. Besides, when an obtainment location of sensorinformation held by client device 2002 is separated from a currentlocation by a certain distance, client device 2002 may delete the sensorinformation from storage 2012. In other words, when a difference betweena current position of the mobile object or a sensor and a position ofthe mobile object or the sensor when the sensor obtained sensorinformation exceeds a predetermined distance, client device 2002 maydelete the sensor information from storage 2012. Accordingly, it ispossible to reduce the capacity of storage 2012 of client device 2002.

When client device 2002 does not finish obtaining sensor information (NOin S2005), client device 2002 performs step S2001 and the subsequentsteps again. Further, when client device 2002 finishes obtaining sensorinformation (YES in S2005), client device 2002 completes the process.

Client device 2002 may select sensor information to be transmitted toserver 2001, in accordance with communication conditions. For example,when high-speed communication is available, client device 2002preferentially transmits sensor information (e.g., information obtainedby LiDAR) of which the data size held in storage 2012 is large.Additionally, when high-speed communication is not readily available,client device 2002 transmits sensor information (e.g., a visible lightimage) which has high priority and of which the data size held instorage 2012 is small. Accordingly, client device 2002 can efficientlytransmit sensor information held in storage 2012, in accordance withnetwork conditions

Client device 2002 may obtain, from server 2001, time informationindicating a current time and location information indicating a currentlocation. Moreover, client device 2002 may determine an obtainment timeand an obtainment location of sensor information based on the obtainedtime information and location information. In other words, client device2002 may obtain time information from server 2001 and generateobtainment time information using the obtained time information. Clientdevice 2002 may also obtain location information from server 2001 andgenerate obtainment location information using the obtained locationinformation.

For example, regarding time information, server 2001 and client device2002 perform clock synchronization using a means such as the NetworkTime Protocol (NTP) or the Precision Time Protocol (PTP). This enablesclient device 2002 to obtain accurate time information. What's more,since it is possible to synchronize clocks between server 2001 andclient devices 2002, it is possible to synchronize times included inpieces of sensor information obtained by separate client devices 2002.As a result, server 2001 can handle sensor information indicating asynchronized time. It should be noted that a means of synchronizingclocks may be any means other than the NTP or PTP. In addition, GPSinformation may be used as the time information and the locationinformation.

Server 2001 may specify a time or a location and obtain pieces of sensorinformation from client devices 2002. For example, when an accidentoccurs, in order to search for a client device in the vicinity of theaccident, server 2001 specifies an accident occurrence time and anaccident occurrence location and broadcasts sensor informationtransmission requests to client devices 2002. Then, client device 2002having sensor information obtained at the corresponding time andlocation transmits the sensor information to server 2001. In otherwords, client device 2002 receives, from server 2001, a sensorinformation transmission request including specification informationspecifying a location and a time. When sensor information obtained at alocation and a time indicated by the specification information is storedin storage 2012, and client device 2002 determines that the mobileobject is present in the environment in which the mobile object cantransmit the sensor information to server 2001, client device 2002transmits, to server 2001, the sensor information obtained at thelocation and the time indicated by the specification information.Consequently, server 2001 can obtain the pieces of sensor informationpertaining to the occurrence of the accident from client devices 2002,and use the pieces of sensor information for accident analysis etc.

It should be noted that when client device 2002 receives a sensorinformation transmission request from server 2001, client device 2002may refuse to transmit sensor information. Additionally, client device2002 may set in advance which pieces of sensor information can betransmitted. Alternatively, server 2001 may inquire of client device2002 each time whether sensor information can be transmitted.

A point may be given to client device 2002 that has transmitted sensorinformation to server 2001. This point can be used in payment for, forexample, gasoline expenses, electric vehicle (EV) charging expenses, ahighway toll, or rental car expenses. After obtaining sensorinformation, server 2001 may delete information for specifying clientdevice 2002 that has transmitted the sensor information. For example,this information is a network address of client device 2002. Since thisenables the anonymization of sensor information, a user of client device2002 can securely transmit sensor information from client device 2002 toserver 2001. Server 2001 may include servers. For example, by serverssharing sensor information, even when one of the servers breaks down,the other servers can communicate with client device 2002. Accordingly,it is possible to avoid service outage due to a server breakdown.

A specified location specified by a sensor information transmissionrequest indicates an accident occurrence location etc., and may bedifferent from a position of client device 2002 at a specified timespecified by the sensor information transmission request. For thisreason, for example, by specifying, as a specified location, a rangesuch as within XX meters of a surrounding area, server 2001 can requestinformation from client device 2002 within the range. Similarly, server2001 may also specify, as a specified time, a range such as within Nseconds before and after a certain time. As a result, server 2001 canobtain sensor information from client device 2002 present for a timefrom t-N to t+N and in a location within XX meters from absoluteposition S. When client device 2002 transmits three-dimensional datasuch as LiDAR, client device 2002 may transmit data created immediatelyafter time t.

Server 2001 may separately specify information indicating, as aspecified location, a location of client device 2002 from which sensorinformation is to be obtained, and a location at which sensorinformation is desirably obtained. For example, server 2001 specifiesthat sensor information including at least a range within YY meters fromabsolute position S is to be obtained from client device 2002 presentwithin XX meters from absolute position S. When client device 2002selects three-dimensional data to be transmitted, client device 2002selects one or more pieces of three-dimensional data so that the one ormore pieces of three-dimensional data include at least the sensorinformation including the specified range. Each of the one or morepieces of three-dimensional data is a random-accessible unit of data. Inaddition, when client device 2002 transmits a visible light image,client device 2002 may transmit pieces of temporally continuous imagedata including at least a frame immediately before or immediately aftertime t.

When client device 2002 can use physical networks such as 5G, Wi-Fi, ormodes in 5G for transmitting sensor information, client device 2002 mayselect a network to be used according to the order of priority notifiedby server 2001. Alternatively, client device 2002 may select a networkthat enables client device 2002 to ensure an appropriate bandwidth basedon the size of transmit data. Alternatively, client device 2002 mayselect a network to be used, based on data transmission expenses etc. Atransmission request from server 2001 may include information indicatinga transmission deadline, for example, performing transmission whenclient device 2002 can start transmission by time t. When server 2001cannot obtain sufficient sensor information within a time limit, server2001 may issue a transmission request again.

Sensor information may include header information indicatingcharacteristics of sensor data along with compressed or uncompressedsensor data. Client device 2002 may transmit header information toserver 2001 via a physical network or a communication protocol that isdifferent from a physical network or a communication protocol used forsensor data. For example, client device 2002 transmits headerinformation to server 2001 prior to transmitting sensor data. Server2001 determines whether to obtain the sensor data of client device 2002,based on a result of analysis of the header information. For example,header information may include information indicating a point cloudobtainment density, an elevation angle, or a frame rate of LiDAR, orinformation indicating, for example, a resolution, an SN ratio, or aframe rate of a visible light image. Accordingly, server 2001 can obtainthe sensor information from client device 2002 having the sensor data ofdetermined quality.

As stated above, client device 2002 is provided in the mobile object,obtains sensor information that has been obtained by a sensor providedin the mobile object and indicates a surrounding state of the mobileobject, and stores the sensor information into storage 2012. Clientdevice 2002 determines whether the mobile object is present in anenvironment in which the mobile object is capable of transmitting thesensor information to server 2001, and transmits the sensor informationto server 2001 when the mobile object is determined to be present in theenvironment in which the mobile object is capable of transmitting thesensor information to server 2001.

Additionally, client device 2002 further creates, from the sensorinformation, three-dimensional data of a surrounding area of the mobileobject, and estimates a self-location of the mobile object using thethree-dimensional data created.

Besides, client device 2002 further transmits a transmission request fora three-dimensional map to server 2001, and receives thethree-dimensional map from server 2001. In the estimating, client device2002 estimates the self-location using the three-dimensional data andthe three-dimensional map.

It should be noted that the above process performed by client device2002 may be realized as an information transmission method for use inclient device 2002.

In addition, client device 2002 may include a processor and memory.Using the memory, the processor may perform the above process.

Next, a sensor information collection system according to the presentembodiment will be described. FIG. 135 is a diagram illustrating aconfiguration of the sensor information collection system according tothe present embodiment. As illustrated in FIG. 135 , the sensorinformation collection system according to the present embodimentincludes terminal 2021A, terminal 2021B, communication device 2022A,communication device 2022B, network 2023, data collection server 2024,map server 2025, and client device 2026. It should be noted that whenterminal 2021A and terminal 2021B are not particularly distinguished,terminal 2021A and terminal 2021B are also referred to as terminal 2021.Additionally, when communication device 2022A and communication device2022B are not particularly distinguished, communication device 2022A andcommunication device 2022B are also referred to as communication device2022.

Data collection server 2024 collects data such as sensor data obtainedby a sensor included in terminal 2021 as position-related data in whichthe data is associated with a position in a three-dimensional space.

Sensor data is data obtained by, for example, detecting a surroundingstate of terminal 2021 or an internal state of terminal 2021 using asensor included in terminal 2021. Terminal 2021 transmits, to datacollection server 2024, one or more pieces of sensor data collected fromone or more sensor devices in locations at which direct communicationwith terminal 2021 is possible or at which communication with terminal2021 is possible by the same communication system or via one or morerelay devices.

Data included in position-related data may include, for example,information indicating an operating state, an operating log, a serviceuse state, etc. of a terminal or a device included in the terminal. Inaddition, the data include in the position-related data may include, forexample, information in which an identifier of terminal 2021 isassociated with a position or a movement path etc. of terminal 2021.

Information indicating a position included in position-related data isassociated with, for example, information indicating a position inthree-dimensional data such as three-dimensional map data. The detailsof information indicating a position will be described later.

Position-related data may include at least one of the above-describedtime information or information indicating an attribute of data includedin the position-related data or a type (e.g., a model number) of asensor that has created the data, in addition to position informationthat is information indicating a position. The position information andthe time information may be stored in a header area of theposition-related data or a header area of a frame that stores theposition-related data. Further, the position information and the timeinformation may be transmitted and/or stored as metadata associated withthe position-related data, separately from the position-related data.

Map server 2025 is connected to, for example, network 2023, andtransmits three-dimensional data such as three-dimensional map data inresponse to a request from another device such as terminal 2021.Besides, as described in the aforementioned embodiments, map server 2025may have, for example, a function of updating three-dimensional datausing sensor information transmitted from terminal 2021.

Data collection server 2024 is connected to, for example, network 2023,collects position-related data from another device such as terminal2021, and stores the collected position-related data into a storage ofdata collection server 2024 or a storage of another server. In addition,data collection server 2024 transmits, for example, metadata ofcollected position-related data or three-dimensional data generatedbased on the position-related data, to terminal 2021 in response to arequest from terminal 2021.

Network 2023 is, for example, a communication network such as theInternet. Terminal 2021 is connected to network 2023 via communicationdevice 2022. Communication device 2022 communicates with terminal 2021using one communication system or switching between communicationsystems. Communication device 2022 is a communication satellite thatperforms communication using, for example, (1) a base station compliantwith Long-Term Evolution (LTE) etc., (2) an access point (AP) for Wi-Fior millimeter-wave communication etc., (3) a low-power wide-area (LPWA)network gateway such as SIGFOX, LoRaWAN, or Wi-SUN, or (4) a satellitecommunication system such as DVB-S2.

It should be noted that a base station may communicate with terminal2021 using a system classified as an LPWA network such as NarrowbandInternet of Things (NB IoT) or LTE-M, or switching between thesesystems.

Here, although, in the example given, terminal 2021 has a function ofcommunicating with communication device 2022 that uses two types ofcommunication systems, and communicates with map server 2025 or datacollection server 2024 using one of the communication systems orswitching between the communication systems and between communicationdevices 2022 to be a direct communication partner; a configuration ofthe sensor information collection system and terminal 2021 is notlimited to this. For example, terminal 2021 need not have a function ofperforming communication using communication systems, and may have afunction of performing communication using one of the communicationsystems. Terminal 2021 may also support three or more communicationsystems. Additionally, each terminal 2021 may support a differentcommunication system.

Terminal 2021 includes, for example, the configuration of client device902 illustrated in FIG. 125 . Terminal 2021 estimates a self-locationetc. using received three-dimensional data. Besides, terminal 2021associates sensor data obtained from a sensor and position informationobtained by self-location estimation to generate position-related data.

Position information appended to position-related data indicates, forexample, a position in a coordinate system used for three-dimensionaldata. For example, the position information is coordinate valuesrepresented using a value of a latitude and a value of a longitude.Here, terminal 2021 may include, in the position information, acoordinate system serving as a reference for the coordinate values andinformation indicating three-dimensional data used for locationestimation, along with the coordinate values. Coordinate values may alsoinclude altitude information.

The position information may be associated with a data unit or a spaceunit usable for encoding the above three-dimensional data. Such a unitis, for example, WLD, GOS, SPC, VLM, or VXL. Here, the positioninformation is represented by, for example, an identifier foridentifying a data unit such as the SPC corresponding toposition-related data. It should be noted that the position informationmay include, for example, information indicating three-dimensional dataobtained by encoding a three-dimensional space including a data unitsuch as the SPC or information indicating a detailed position within theSPC, in addition to the identifier for identifying the data unit such asthe SPC. The information indicating the three-dimensional data is, forexample, a file name of the three-dimensional data.

As stated above, by generating position-related data associated withposition information based on location estimation usingthree-dimensional data, the system can give more accurate positioninformation to sensor information than when the system appends positioninformation based on a self-location of a client device (terminal 2021)obtained using a GPS to sensor information. As a result, even whenanother device uses the position-related data in another service, thereis a possibility of more accurately determining a position correspondingto the position-related data in an actual space, by performing locationestimation based on the same three-dimensional data.

It should be noted that although the data transmitted from terminal 2021is the position-related data in the example given in the presentembodiment, the data transmitted from terminal 2021 may be dataunassociated with position information. In other words, the transmissionand reception of three-dimensional data or sensor data described in theother embodiments may be performed via network 2023 described in thepresent embodiment.

Next, a different example of position information indicating a positionin a three-dimensional or two-dimensional actual space or in a map spacewill be described. The position information appended to position-relateddata may be information indicating a relative position relative to akeypoint in three-dimensional data. Here, the keypoint serving as areference for the position information is encoded as, for example, SWLD,and notified to terminal 2021 as three-dimensional data.

The information indicating the relative position relative to thekeypoint may be, for example, information that is represented by avector from the keypoint to the point indicated by the positioninformation, and indicates a direction and a distance from the keypointto the point indicated by the position information. Alternatively, theinformation indicating the relative position relative to the keypointmay be information indicating an amount of displacement from thekeypoint to the point indicated by the position information along eachof the x axis, the y axis, and the z axis. Additionally, the informationindicating the relative position relative to the keypoint may beinformation indicating a distance from each of three or more keypointsto the point indicated by the position information. It should be notedthat the relative position need not be a relative position of the pointindicated by the position information represented using each keypoint asa reference, and may be a relative position of each keypoint representedwith respect to the point indicated by the position information.Examples of position information based on a relative position relativeto a keypoint include information for identifying a keypoint to be areference, and information indicating the relative position of the pointindicated by the position information and relative to the keypoint. Whenthe information indicating the relative position relative to thekeypoint is provided separately from three-dimensional data, theinformation indicating the relative position relative to the keypointmay include, for example, coordinate axes used in deriving the relativeposition, information indicating a type of the three-dimensional data,and/or information indicating a magnitude per unit amount (e.g., ascale) of a value of the information indicating the relative position.

The position information may include, for each keypoint, informationindicating a relative position relative to the keypoint. When theposition information is represented by relative positions relative tokeypoints, terminal 2021 that intends to identify a position in anactual space indicated by the position information may calculatecandidate points of the position indicated by the position informationfrom positions of the keypoints each estimated from sensor data, and maydetermine that a point obtained by averaging the calculated candidatepoints is the point indicated by the position information. Since thisconfiguration reduces the effects of errors when the positions of thekeypoints are estimated from the sensor data, it is possible to improvethe estimation accuracy for the point in the actual space indicated bythe position information. Besides, when the position informationincludes information indicating relative positions relative tokeypoints, if it is possible to detect any one of the keypointsregardless of the presence of keypoints undetectable due to a limitationsuch as a type or performance of a sensor included in terminal 2021, itis possible to estimate a value of the point indicated by the positioninformation.

A point identifiable from sensor data can be used as a keypoint.Examples of the point identifiable from the sensor data include a pointor a point within a region that satisfies a predetermined keypointdetection condition, such as the above-described three-dimensionalfeature or feature of visible light data is greater than or equal to athreshold value.

Moreover, a marker etc. placed in an actual space may be used as akeypoint. In this case, the maker may be detected and located from dataobtained using a sensor such as LiDAR or a camera. For example, themarker may be represented by a change in color or luminance value(degree of reflection), or a three-dimensional shape (e.g., unevenness).Coordinate values indicating a position of the marker, or atwo-dimensional bar code or a bar code etc. generated from an identifierof the marker may be also used.

Furthermore, a light source that transmits an optical signal may be usedas a marker. When a light source of an optical signal is used as amarker, not only information for obtaining a position such as coordinatevalues or an identifier but also other data may be transmitted using anoptical signal. For example, an optical signal may include contents ofservice corresponding to the position of the marker, an address forobtaining contents such as a URL, or an identifier of a wirelesscommunication device for receiving service, and information indicating awireless communication system etc. for connecting to the wirelesscommunication device. The use of an optical communication device (alight source) as a marker not only facilitates the transmission of dataother than information indicating a position but also makes it possibleto dynamically change the data.

Terminal 2021 finds out a correspondence relationship of keypointsbetween mutually different data using, for example, a common identifierused for the data, or information or a table indicating thecorrespondence relationship of the keypoints between the data. Whenthere is no information indicating a correspondence relationship betweenkeypoints, terminal 2021 may also determine that when coordinates of akeypoint in three-dimensional data are converted into a position in aspace of another three-dimensional data, a keypoint closest to theposition is a corresponding keypoint.

When the position information based on the relative position describedabove is used, terminal 2021 that uses mutually differentthree-dimensional data or services can identify or estimate a positionindicated by the position information with respect to a common keypointincluded in or associated with each three-dimensional data. As a result,terminal 2021 that uses the mutually different three-dimensional data orthe services can identify or estimate the same position with higheraccuracy.

Even when map data or three-dimensional data represented using mutuallydifferent coordinate systems are used, since it is possible to reducethe effects of errors caused by the conversion of a coordinate system,it is possible to coordinate services based on more accurate positioninformation.

Hereinafter, an example of functions provided by data collection server2024 will be described. Data collection server 2024 may transferreceived position-related data to another data server. When there aredata servers, data collection server 2024 determines to which dataserver received position-related data is to be transferred, andtransfers the position-related data to a data server determined as atransfer destination.

Data collection server 2024 determines a transfer destination based on,for example, transfer destination server determination rules preset todata collection server 2024. The transfer destination serverdetermination rules are set by, for example, a transfer destinationtable in which identifiers respectively associated with terminals 2021are associated with transfer destination data servers.

Terminal 2021 appends an identifier associated with terminal 2021 toposition-related data to be transmitted, and transmits theposition-related data to data collection server 2024. Data collectionserver 2024 determines a transfer destination data server correspondingto the identifier appended to the position-related data, based on thetransfer destination server determination rules set out using thetransfer destination table etc.; and transmits the position-related datato the determined data server. The transfer destination serverdetermination rules may be specified based on a determination conditionset using a time, a place, etc. at which position-related data isobtained. Here, examples of the identifier associated with transmissionsource terminal 2021 include an identifier unique to each terminal 2021or an identifier indicating a group to which terminal 2021 belongs.

The transfer destination table need not be a table in which identifiersassociated with transmission source terminals are directly associatedwith transfer destination data servers. For example, data collectionserver 2024 holds a management table that stores tag informationassigned to each identifier unique to terminal 2021, and a transferdestination table in which the pieces of tag information are associatedwith transfer destination data servers. Data collection server 2024 maydetermine a transfer destination data server based on tag information,using the management table and the transfer destination table. Here, thetag information is, for example, control information for management orcontrol information for providing service assigned to a type, a modelnumber, an owner of terminal 2021 corresponding to the identifier, agroup to which terminal 2021 belongs, or another identifier. Moreover,in the transfer destination able, identifiers unique to respectivesensors may be used instead of the identifiers associated withtransmission source terminals 2021. Furthermore, the transferdestination server determination rules may be set by client device 2026.

Data collection server 2024 may determine data servers as transferdestinations, and transfer received position-related data to the dataservers. According to this configuration, for example, whenposition-related data is automatically backed up or when, in order thatposition-related data is commonly used by different services, there is aneed to transmit the position-related data to a data server forproviding each service, it is possible to achieve data transfer asintended by changing a setting of data collection server 2024. As aresult, it is possible to reduce the number of steps necessary forbuilding and changing a system, compared to when a transmissiondestination of position-related data is set for each terminal 2021.

Data collection server 2024 may register, as a new transfer destination,a data server specified by a transfer request signal received from adata server; and transmit position-related data subsequently received tothe data server, in response to the transfer request signal.

Data collection server 2024 may store position-related data receivedfrom terminal 2021 into a recording device, and transmitposition-related data specified by a transmission request signalreceived from terminal 2021 or a data server to request source terminal2021 or the data server in response to the transmission request signal.

Data collection server 2024 may determine whether position-related datais suppliable to a request source data server or terminal 2021, andtransfer or transmit the position-related data to the request sourcedata server or terminal 2021 when determining that the position-relateddata is suppliable.

When data collection server 2024 receives a request for currentposition-related data from client device 2026, even if it is not atiming for transmitting position-related data by terminal 2021, datacollection server 2024 may send a transmission request for the currentposition-related data to terminal 2021, and terminal 2021 may transmitthe current position-related data in response to the transmissionrequest.

Although terminal 2021 transmits position information data to datacollection server 2024 in the above description, data collection server2024 may have a function of managing terminal 2021 such as a functionnecessary for collecting position-related data from terminal 2021 or afunction used when collecting position-related data from terminal 2021.

Data collection server 2024 may have a function of transmitting, toterminal 2021, a data request signal for requesting transmission ofposition information data, and collecting position-related data.

Management information such as an address for communicating withterminal 2021 from which data is to be collected or an identifier uniqueto terminal 2021 is registered in advance in data collection server2024. Data collection server 2024 collects position-related data fromterminal 2021 based on the registered management information. Managementinformation may include information such as types of sensors included interminal 2021, the number of sensors included in terminal 2021, andcommunication systems supported by terminal 2021.

Data collection server 2024 may collect information such as an operatingstate or a current position of terminal 2021 from terminal 2021.

Registration of management information may be instructed by clientdevice 2026, or a process for the registration may be started byterminal 2021 transmitting a registration request to data collectionserver 2024. Data collection server 2024 may have a function ofcontrolling communication between data collection server 2024 andterminal 2021.

Communication between data collection server 2024 and terminal 2021 maybe established using a dedicated line provided by a service providersuch as a mobile network operator (MNO) or a mobile virtual networkoperator (MVNO), or a virtual dedicated line based on a virtual privatenetwork (VPN). According to this configuration, it is possible toperform secure communication between terminal 2021 and data collectionserver 2024.

Data collection server 2024 may have a function of authenticatingterminal 2021 or a function of encrypting data to be transmitted andreceived between data collection server 2024 and terminal 2021. Here,the authentication of terminal 2021 or the encryption of data isperformed using, for example, an identifier unique to terminal 2021 oran identifier unique to a terminal group including terminals 2021, whichis shared in advance between data collection server 2024 and terminal2021. Examples of the identifier include an international mobilesubscriber identity (IMSI) that is a unique number stored in asubscriber identity module (SIM) card. An identifier for use inauthentication and an identifier for use in encryption of data may beidentical or different.

The authentication or the encryption of data between data collectionserver 2024 and terminal 2021 is feasible when both data collectionserver 2024 and terminal 2021 have a function of performing the process.The process does not depend on a communication system used bycommunication device 2022 that performs relay. Accordingly, since it ispossible to perform the common authentication or encryption withoutconsidering whether terminal 2021 uses a communication system, theuser's convenience of system architecture is increased. However, theexpression “does not depend on a communication system used bycommunication device 2022 that performs relay” means a change accordingto a communication system is not essential. In other words, in order toimprove the transfer efficiency or ensure security, the authenticationor the encryption of data between data collection server 2024 andterminal 2021 may be changed according to a communication system used bya relay device.

Data collection server 2024 may provide client device 2026 with a UserInterface (UI) that manages data collection rules such as types ofposition-related data collected from terminal 2021 and data collectionschedules. Accordingly, a user can specify, for example, terminal 2021from which data is to be collected using client device 2026, a datacollection time, and a data collection frequency. Additionally, datacollection server 2024 may specify, for example, a region on a map fromwhich data is to be desirably collected, and collect position-relateddata from terminal 2021 included in the region.

When the data collection rules are managed on a per terminal 2021 basis,client device 2026 presents, on a screen, a list of terminals 2021 orsensors to be managed. The user sets, for example, a necessity for datacollection or a collection schedule for each item in the list.

When a region on a map from which data is to be desirably collected isspecified, client device 2026 presents, on a screen, a two-dimensionalor three-dimensional map of a region to be managed. The user selects theregion from which data is to be collected on the displayed map. Examplesof the region selected on the map include a circular or rectangularregion having a point specified on the map as the center, or a circularor rectangular region specifiable by a drag operation. Client device2026 may also select a region in a preset unit such as a city, an areaor a block in a city, or a main road, etc. Instead of specifying aregion using a map, a region may be set by inputting values of alatitude and a longitude, or a region may be selected from a list ofcandidate regions derived based on inputted text information. Textinformation is, for example, a name of a region, a city, or a landmark.

Moreover, data may be collected while the user dynamically changes aspecified region by specifying one or more terminals 2021 and setting acondition such as within 100 meters of one or more terminals 2021.

When client device 2026 includes a sensor such as a camera, a region ona map may be specified based on a position of client device 2026 in anactual space obtained from sensor data. For example, client device 2026may estimate a self-location using sensor data, and specify, as a regionfrom which data is to be collected, a region within a predetermineddistance from a point on a map corresponding to the estimated locationor a region within a distance specified by the user. Client device 2026may also specify, as the region from which the data is to be collected,a sensing region of the sensor, that is, a region corresponding toobtained sensor data. Alternatively, client device 2026 may specify, asthe region from which the data is to be collected, a region based on alocation corresponding to sensor data specified by the user. Eitherclient device 2026 or data collection server 2024 may estimate a regionon a map or a location corresponding to sensor data.

When a region on a map is specified, data collection server 2024 mayspecify terminal 2021 within the specified region by collecting currentposition information of each terminal 2021, and may send a transmissionrequest for position-related data to specified terminal 2021. When datacollection server 2024 transmits information indicating a specifiedregion to terminal 2021, determines whether terminal 2021 is presentwithin the specified region, and determines that terminal 2021 ispresent within the specified region, rather than specifying terminal2021 within the region, terminal 2021 may transmit position-relateddata.

Data collection server 2024 transmits, to client device 2026, data suchas a list or a map for providing the above-described User Interface (UI)in an application executed by client device 2026. Data collection server2024 may transmit, to client device 2026, not only the data such as thelist or the map but also an application program. Additionally, the aboveUI may be provided as contents created using HTML displayable by abrowser. It should be noted that part of data such as map data may besupplied from a server, such as map server 2025, other than datacollection server 2024.

When client device 2026 receives an input for notifying the completionof an input such as pressing of a setup key by the user, client device2026 transmits the inputted information as configuration information todata collection server 2024. Data collection server 2024 transmits, toeach terminal 2021, a signal for requesting position-related data ornotifying position-related data collection rules, based on theconfiguration information received from client device 2026, and collectsthe position-related data.

Next, an example of controlling operation of terminal 2021 based onadditional information added to three-dimensional or two-dimensional mapdata will be described.

In the present configuration, object information that indicates aposition of a power feeding part such as a feeder antenna or a feedercoil for wireless power feeding buried under a road or a parking lot isincluded in or associated with three-dimensional data, and such objectinformation is provided to terminal 2021 that is a vehicle or a drone.

A vehicle or a drone that has obtained the object information to getcharged automatically moves so that a position of a charging part suchas a charging antenna or a charging coil included in the vehicle or thedrone becomes opposite to a region indicated by the object information,and such vehicle or a drone starts to charge itself. It should be notedthat when a vehicle or a drone has no automatic driving function, adirection to move in or an operation to perform is presented to a driveror an operator by using an image displayed on a screen, audio, etc. Whena position of a charging part calculated based on an estimatedself-location is determined to fall within the region indicated by theobject information or a predetermined distance from the region, an imageor audio to be presented is changed to a content that puts a stop todriving or operating, and the charging is started.

Object information need not be information indicating a position of apower feeding part, and may be information indicating a region withinwhich placement of a charging part results in a charging efficiencygreater than or equal to a predetermined threshold value. A positionindicated by object information may be represented by, for example, thecentral point of a region indicated by the object information, a regionor a line within a two-dimensional plane, or a region, a line, or aplane within a three-dimensional space.

According to this configuration, since it is possible to identify theposition of the power feeding antenna unidentifiable by sensing data ofLiDAR or an image captured by the camera, it is possible to highlyaccurately align a wireless charging antenna included in terminal 2021such as a vehicle with a wireless power feeding antenna buried under aroad. As a result, it is possible to increase a charging speed at thetime of wireless charging and improve the charging efficiency.

Object information may be an object other than a power feeding antenna.For example, three-dimensional data includes, for example, a position ofan AP for millimeter-wave wireless communication as object information.Accordingly, since terminal 2021 can identify the position of the AP inadvance, terminal 2021 can steer a directivity of beam to a direction ofthe object information and start communication. As a result, it ispossible to improve communication quality such as increasingtransmission rates, reducing the duration of time before startingcommunication, and extending a communicable period.

Object information may include information indicating a type of anobject corresponding to the object information. In addition, whenterminal 2021 is present within a region in an actual spacecorresponding to a position in three-dimensional data of the objectinformation or within a predetermined distance from the region, theobject information may include information indicating a process to beperformed by terminal 2021.

Object information may be provided by a server different from a serverthat provides three-dimensional data. When object information isprovided separately from three-dimensional data, object groups in whichobject information used by the same service is stored may be eachprovided as separate data according to a type of a target service or atarget device.

Three-dimensional data used in combination with object information maybe point cloud data of WLD or keypoint data of SWLD.

In the three-dimensional data encoding device, when attributeinformation of a current three-dimensional point to be encoded islayer-encoded using Levels of Detail (LoDs), the three-dimensional datadecoding device may decode the attribute information in layers down toLoD required by the three-dimensional data decoding device and need notdecode the attribute information in layers not required. For example,when the total number of LoDs for the attribute information in abitstream generated by the three-dimensional data encoding device is N,the three-dimensional data decoding device may decode M LoDs (M<N),i.e., layers from the uppermost layer LoD0 to LoD(M−1), and need notdecode the remaining LoDs, i.e., layers down to LoD(N−1). With this,while reducing the processing load, the three-dimensional data decodingdevice can decode the attribute information in layers from LoD0 toLoD(M−1) required by the three-dimensional data decoding device.

FIG. 136 is a diagram illustrating the foregoing use case. In theexample shown in FIG. 136 , a server stores a three-dimensional mapobtained by encoding three-dimensional geometry information andattribute information. The server (the three-dimensional data encodingdevice) broadcasts the three-dimensional map to client devices (thethree-dimensional data decoding devices: for example, vehicles, drones,etc.) in an area managed by the server, and each client device uses thethree-dimensional map received from the server to perform a process foridentifying the self-position of the client device or a process fordisplaying map information to a user or the like who operates the clientdevice.

The following describes an example of the operation in this case. First,the server encodes the geometry information of the three-dimensional mapusing an octree structure or the like. Then, the sever layer-encodes theattribute information of the three-dimensional map using N LoDsestablished based on the geometry information. The server stores abitstream of the three-dimensional map obtained by the layer-encoding.

Next, in response to a send request for the map information from theclient device in the area managed by the server, the server sends thebitstream of the encoded three-dimensional map to the client device.

The client device receives the bitstream of the three-dimensional mapsent from the server, and decodes the geometry information and theattribute information of the three-dimensional map in accordance withthe intended use of the client device. For example, when the clientdevice performs highly accurate estimation of the self-position usingthe geometry information and the attribute information in N LoDs, theclient device determines that a decoding result to the densethree-dimensional points is necessary as the attribute information, anddecodes all the information in the bitstream.

Moreover, when the client device displays the three-dimensional mapinformation to a user or the like, the client device determines that adecoding result to the sparse three-dimensional points is necessary asthe attribute information, and decodes the geometry information and theattribute information in M LoDs (M<N) starting from an upper layer LoD0.

In this way, the processing load of the client device can be reduced bychanging LoDs for the attribute information to be decoded in accordancewith the intended use of the client device.

In the example shown in FIG. 136 , for example, the three-dimensionalmap includes geometry information and attribute information. Thegeometry information is encoded using the octree. The attributeinformation is encoded using N LoDs.

Client device A performs highly accurate estimation of theself-position. In this case, client device A determines that all thegeometry information and all the attribute information are necessary,and decodes all the geometry information and all the attributeinformation constructed from N LoDs in the bitstream.

Client device B displays the three-dimensional map to a user. In thiscase, client device B determines that the geometry information and theattribute information in M LoDs (M<N) are necessary, and decodes thegeometry information and the attribute information constructed from MLoDs in the bitstream.

It is to be noted that the server may broadcast the three-dimensionalmap to the client devices, or multicast or unicast the three-dimensionalmap to the client devices.

The following describes a variation of the system according to thepresent embodiment. In the three-dimensional data encoding device, whenattribute information of a current three-dimensional point to be encodedis layer-encoded using LoDs, the three-dimensional data encoding devicemay encode the attribute information in layers down to LoD required bythe three-dimensional data decoding device and need not encode theattribute information in layers not required. For example, when thetotal number of LoDs is N, the three-dimensional data encoding devicemay generate a bitstream by encoding M LoDs (M<N), i.e., layers from theuppermost layer LoD0 to LoD(M−1), and not encoding the remaining LoDs,i.e., layers down to LoD(N−1). With this, in response to a request fromthe three-dimensional data decoding device, the three-dimensional dataencoding device can provide a bitstream in which the attributeinformation from LoD0 to LoD(M−1) required by the three-dimensional datadecoding device is encoded.

FIG. 137 is a diagram illustrating the foregoing use case. In theexample shown in FIG. 137 , a server stores a three-dimensional mapobtained by encoding three-dimensional geometry information andattribute information. The server (the three-dimensional data encodingdevice) unicasts, in response to a request from the client device, thethree-dimensional map to a client device (the three-dimensional datadecoding device: for example, a vehicle, a drone, etc.) in an areamanaged by the server, and the client device uses the three-dimensionalmap received from the server to perform a process for identifying theself-position of the client device or a process for displaying mapinformation to a user or the like who operates the client device.

The following describes an example of the operation in this case. First,the server encodes the geometry information of the three-dimensional mapusing an octree structure, or the like. Then, the sever generates abitstream of three-dimensional map A by layer-encoding the attributeinformation of the three-dimensional map using N LoDs established basedon the geometry information, and stores the generated bitstream in theserver. The sever also generates a bitstream of three-dimensional map Bby layer-encoding the attribute information of the three-dimensional mapusing M LoDs (M<N) established based on the geometry information, andstores the generated bitstream in the server.

Next, the client device requests the server to send thethree-dimensional map in accordance with the intended use of the clientdevice. For example, when the client device performs highly accurateestimation of the self-position using the geometry information and theattribute information in N LoDs, the client device determines that adecoding result to the dense three-dimensional points is necessary asthe attribute information, and requests the server to send the bitstreamof three-dimensional map A. Moreover, when the client device displaysthe three-dimensional map information to a user or the like, the clientdevice determines that a decoding result to the sparse three-dimensionalpoints is necessary as the attribute information, and requests theserver to send the bitstream of three-dimensional map B including thegeometry information and the attribute information in M LoDs (M<N)starting from an upper layer LoD0. Then, in response to the send requestfor the map information from the client device, the server sends thebitstream of encoded three-dimensional map A or B to the client device.

The client device receives the bitstream of three-dimensional map A or Bsent from the server in accordance with the intended use of the clientdevice, and decodes the received bitstream. In this way, the serverchanges a bitstream to be sent, in accordance with the intended use ofthe client device. With this, it is possible to reduce the processingload of the client device.

In the example shown in FIG. 137 , the server stores three-dimensionalmap A and three-dimensional map B. The server generatesthree-dimensional map A by encoding the geometry information of thethree-dimensional map using, for example, an octree structure, andencoding the attribute information of the three-dimensional map using NLoDs. In other words, NumLoD included in the bitstream ofthree-dimensional map A indicates N.

The server also generates three-dimensional map B by encoding thegeometry information of the three-dimensional map using, for example, anoctree structure, and encoding the attribute information of thethree-dimensional map using M LoDs. In other words, NumLoD included inthe bitstream of three-dimensional map B indicates M.

Client device A performs highly accurate estimation of theself-position. In this case, client device A determines that all thegeometry information and all the attribute information are necessary,and requests the server to send three-dimensional map A including allthe geometry information and the attribute information constructed fromN LoDs. Client device A receives three-dimensional map A, and decodesall the geometry information and the attribute information constructedfrom N LoDs.

Client device B displays the three-dimensional map to a user. In thiscase, client device B determines that all the geometry information andthe attribute information in M LoDs (M<N) are necessary, and requeststhe server to send three-dimensional map B including all the geometryinformation and the attribute information constructed from M LoDs.Client device B receives three-dimensional map B, and decodes all thegeometry information and the attribute information constructed from MLoDs.

It is to be noted that in addition to three-dimensional map B, theserver (the three-dimensional data encoding device) may generatethree-dimensional map C in which attribute information in the remainingN-M LoDs is encoded, and send three-dimensional map C to client device Bin response to the request from client device B. Moreover, client deviceB may obtain the decoding result of N LoDs using the bitstreams ofthree-dimensional maps B and C.

Hereinafter, an example of an application process will be described.FIG. 138 is a flowchart illustrating an example of the applicationprocess. When an application operation is started, a three-dimensionaldata demultiplexing device obtains an ISOBMFF file including point clouddata and a plurality of pieces of encoded data (S7301). For example, thethree-dimensional data demultiplexing device may obtain the ISOBMFF filethrough communication, or may read the ISOBMFF file from the accumulateddata.

Next, the three-dimensional data demultiplexing device analyzes thegeneral configuration information in the ISOBMFF file, and specifies thedata to be used for the application (S7302). For example, thethree-dimensional data demultiplexing device obtains data that is usedfor processing, and does not obtain data that is not used forprocessing.

Next, the three-dimensional data demultiplexing device extracts one ormore pieces of data to be used for the application, and analyzes theconfiguration information on the data (S7303).

When the type of the data is encoded data (encoded data in S7304), thethree-dimensional data demultiplexing device converts the ISOBMFF to anencoded stream, and extracts a timestamp (S7305). Additionally, thethree-dimensional data demultiplexing device refers to, for example, theflag indicating whether or not the synchronization between data isaligned to determine whether or not the synchronization between data isaligned, and may perform a synchronization process when not aligned.

Next, the three-dimensional data demultiplexing device decodes the datawith a predetermined method according to the timestamp and the otherinstructions, and processes the decoded data (S7306).

On the other hand, when the type of the data is RAW data (RAW data inS7304), the three-dimensional data demultiplexing device extracts thedata and timestamp (S7307). Additionally, the three-dimensional datademultiplexing device may refer to, for example, the flag indicatingwhether or not the synchronization between data is aligned to determinewhether or not the synchronization between data is aligned, and mayperform a synchronization process when not aligned. Next, thethree-dimensional data demultiplexing device processes the dataaccording to the timestamp and the other instructions (S7308).

For example, an example will be described in which the sensor signalsobtained by a beam LiDAR, a FLASH LiDAR, and a camera are encoded andmultiplexed with respective different encoding schemes. FIG. 139 is adiagram illustrating examples of the sensor ranges of a beam LiDAR, aFLASH LiDAR, and a camera. For example, the beam LiDAR detects alldirections in the periphery of a vehicle (sensor), and the FLASH LiDARand the camera detect the range in one direction (for example, thefront) of the vehicle.

In the case of an application that integrally handles a LiDAR pointcloud, the three-dimensional data demultiplexing device refers to thegeneral configuration information, and extracts and decodes the encodeddata of the beam LiDAR and the FLASH LiDAR. Additionally, thethree-dimensional data demultiplexing device does not extract cameraimages.

According to the timestamps of the beam LiDAR and the FLASH LiDAR, thethree-dimensional data demultiplexing device simultaneously processesthe respective encoded data of the time of the same timestamp.

For example, the three-dimensional data demultiplexing device maypresent the processed data with a presentation device, may synthesizethe point cloud data of the beam LiDAR and the FLASH LiDAR, or mayperform a process such as rendering.

Additionally, in the case of an application that performs calibrationbetween data, the three-dimensional data demultiplexing device mayextract sensor geometry information, and use the sensor geometryinformation in the application.

For example, the three-dimensional data demultiplexing device may selectwhether to use beam LiDAR information or FLASH LiDAR information in theapplication, and may switch the process according to the selectionresult.

In this manner, since it is possible to adaptively change the obtainingof data and the encoding process according to the process of theapplication, the processing amount and the power consumption can bereduced.

Hereinafter, a use case in automated driving will be described. FIG. 140is a diagram illustrating a configuration example of an automateddriving system. This automated driving system includes cloud server7350, and edge 7360 such as an in-vehicle device or a mobile device.Cloud server 7350 includes demultiplexer 7351, decoders 7352A, 7352B,and 7355, point cloud data synthesizer 7353, large data accumulator7354, comparator 7356, and encoder 7357. Edge 7360 includes sensors7361A and 7361B, point cloud data generators 7362A and 7362B,synchronizer 7363, encoders 7364A and 7364B, multiplexer 7365, updatedata accumulator 7366, demultiplexer 7367, decoder 7368, filter 7369,self-position estimator 7370, and driving controller 7371.

In this system, edge 7360 downloads large data, which is largepoint-cloud map data accumulated in cloud server 7350. Edge 7360performs a self-position estimation process of edge 7360 (a vehicle or aterminal) by matching the large data with the sensor informationobtained by edge 7360. Additionally, edge 7360 uploads the obtainedsensor information to cloud server 7350, and updates the large data tothe latest map data.

Additionally, in various applications that handle point cloud data inthe system, point cloud data with different encoding methods arehandled.

Cloud server 7350 encodes and multiplexes large data. Specifically,encoder 7357 performs encoding by using a third encoding method suitablefor encoding a large point cloud. Additionally, encoder 7357 multiplexesencoded data. Large data accumulator 7354 accumulates the data encodedand multiplexed by encoder 7357.

Edge 7360 performs sensing. Specifically, point cloud data generator7362A generates first point cloud data (geometry information (geometry)and attribute information) by using the sensing information obtained bysensor 7361A. Point cloud data generator 7362B generates second pointcloud data (geometry information and attribute information) by using thesensing information obtained by sensor 7361B. The generated first pointcloud data and second point cloud data are used for the self-positionestimation or vehicle control of automated driving, or for map updating.In each process, a part of information of the first point cloud data andthe second point cloud data may be used.

Edge 7360 performs the self-position estimation. Specifically, edge 7360downloads large data from cloud server 7350. Demultiplexer 7367 obtainsencoded data by demultiplexing the large data in a file format. Decoder7368 obtains large data, which is large point-cloud map data, bydecoding the obtained encoded data.

Self-position estimator 7370 estimates the self-position in the map of avehicle by matching the obtained large data with the first point clouddata and the second point cloud data generated by point cloud datagenerators 7362A and 7362B. Additionally, driving controller 7371 usesthe matching result or the self-position estimation result for drivingcontrol.

Note that self-position estimator 7370 and driving controller 7371 mayextract specific information, such as geometry information, of the largedata, and may perform processes by using the extracted information.Additionally, filter 7369 performs a process such as correction ordecimation on the first point cloud data and the second point clouddata. Self-position estimator 7370 and driving controller 7371 may usethe first point cloud data and second point cloud data on which theprocess has been performed. Additionally, self-position estimator 7370and driving controller 7371 may use the sensor signals obtained bysensors 7361A and 7361B.

Synchronizer 7363 performs time synchronization and geometry correctionbetween a plurality of sensor signals or the pieces of data of aplurality of pieces of point cloud data. Additionally, synchronizer 7363may correct the geometry information on the sensor signal or point clouddata to match the large data, based on geometry correction informationon the large data and sensor data generated by the self-positionestimation process.

Note that synchronization and geometry correction may be performed notby edge 7360, but by cloud server 7350. In this case, edge 7360 maymultiplex the synchronization information and the geometry informationto transmit the synchronization information and the geometry informationto cloud server 7350.

Edge 7360 encodes and multiplexes the sensor signal or point cloud data.Specifically, the sensor signal or point cloud data is encoded by usinga first encoding method or a second encoding method suitable forencoding each signal. For example, encoder 7364A generates first encodeddata by encoding first point cloud data by using the first encodingmethod. Encoder 7364B generates second encoded data by encoding secondpoint cloud data by using the second encoding method.

Multiplexer 7365 generates a multiplexed signal by multiplexing thefirst encoded data, the second encoded data, the synchronizationinformation, and the like. Update data accumulator 7366 accumulates thegenerated multiplexed signal. Additionally, update data accumulator 7366uploads the multiplexed signal to cloud server 7350.

Cloud server 7350 synthesizes the point cloud data. Specifically,demultiplexer 7351 obtains the first encoded data and the second encodeddata by demultiplexing the multiplexed signal uploaded to cloud server7350. Decoder 7352A obtains the first point cloud data (or sensorsignal) by decoding the first encoded data. Decoder 7352B obtains thesecond point cloud data (or sensor signal) by decoding the secondencoded data.

Point cloud data synthesizer 7353 synthesizes the first point cloud dataand the second point cloud data with a predetermined method. When thesynchronization information and the geometry correction information aremultiplexed in the multiplexed signal, point cloud data synthesizer 7353may perform synthesis by using these pieces of information.

Decoder 7355 demultiplexes and decodes the large data accumulated inlarge data accumulator 7354. Comparator 7356 compares the point clouddata generated based on the sensor signal obtained by edge 7360 with thelarge data held by cloud server 7350, and determines the point clouddata that needs to be updated. Comparator 7356 updates the point clouddata that is determined to need to be updated of the large data to thepoint cloud data obtained from edge 7360.

Encoder 7357 encodes and multiplexes the updated large data, andaccumulates the obtained data in large data accumulator 7354.

As described above, the signals to be handled may be different, and thesignals to be multiplexed or encoding methods may be different,according to the usage or applications to be used. Even in such a case,flexible decoding and application processes are enabled by multiplexingdata of various encoding schemes by using the present embodiment.Additionally, even in a case where the encoding schemes of signals aredifferent, by conversion to an encoding scheme suitable fordemultiplexing, decoding, data conversion, encoding, and multiplexingprocessing, it becomes possible to build various applications andsystems, and to offer of flexible services.

Hereinafter, an example of decoding and application of divided data willbe described. First, the information on divided data will be described.FIG. 141 is a diagram illustrating a configuration example of abitstream. The general information of divided data indicates, for eachdivided data, the sensor ID (sensor_id) and data ID (data_id) of thedivided data. Note that the data ID is also indicated in the header ofeach encoded data.

Note that the general information of divided data illustrated in FIG.141 includes, in addition to the sensor ID, at least one of the sensorinformation (Sensor), the version (Version) of the sensor, the makername (Maker) of the sensor, the mount information (Mount Info.) of thesensor, and the position coordinates of the sensor (World Coordinate).Accordingly, the three-dimensional data decoding device can obtain theinformation on various sensors from the configuration information.

The general information of divided data may be stored in SPS, GPS, orAPS, which is the metadata, or may be stored in SEI, which is themetadata not required for encoding. Additionally, at the time ofmultiplexing, the three-dimensional data encoding device stores the SEIin a file of ISOBMFF. The three-dimensional data decoding device canobtain desired divided data based on the metadata.

In FIG. 141 , SPS is the metadata of the entire encoded data, GPS is themetadata of the geometry information, APS is the metadata for eachattribute information, G is encoded data of the geometry information foreach divided data, and A1, etc. are encoded data of the attributeinformation for each divided data.

Next, an application example of divided data will be described. Anexample of application will be described in which an arbitrary pointcloud is selected, and the selected point cloud is presented. FIG. 142is a flowchart of a point cloud selection process performed by thisapplication. FIG. 143 to FIG. 145 are diagrams illustrating screenexamples of the point cloud selection process.

As illustrated in FIG. 143 , the three-dimensional data decoding devicethat performs the application includes, for example, a UI unit thatdisplays an input UI (user interface) 8661 for selecting an arbitrarypoint cloud. Input UI 8661 includes presenter 8662 that presents theselected point cloud, and an operation unit (buttons 8663 and 8664) thatreceives operations by a user. After a point cloud is selected in UI8661, the three-dimensional data decoding device obtains desired datafrom accumulator 8665.

First, based on an operation by the user on input UI 8661, the pointcloud information that the user wants to display is selected (S8631).Specifically, by selecting button 8663, the point cloud based on sensor1 is selected. By selecting button 8664, the point cloud based on sensor2 is selected. Alternatively, by selecting both button 8663 and button8664, the point cloud based on sensor 1 and the point cloud based onsensor 2 are selected. Note that it is an example of the selectionmethod of point cloud, and it is not limited to this.

Next, the three-dimensional data decoding device analyzes the generalinformation of divided data included in the multiplexed signal(bitstream) or encoded data, and specifies the data ID (data_id) of thedivided data constituting the selected point cloud from the sensor ID(sensor_id) of the selected sensor (S8632). Next, the three-dimensionaldata decoding device extracts, from the multiplexed signal, the encodeddata including the specified and desired data ID, and decodes theextracted encoded data to decode the point cloud based on the selectedsensor (S8633). Note that the three-dimensional data decoding devicedoes not decode the other encoded data.

Lastly, the three-dimensional data decoding device presents (forexample, displays) the decoded point cloud (S8634). FIG. 144 illustratesan example in the case where button 8663 for sensor 1 is pressed, andthe point cloud of sensor 1 is presented. FIG. 145 illustrates anexample in the case where both button 8663 for sensor 1 and button 8664for sensor 2 are pressed, and the point clouds of sensor 1 and sensor 2are presented.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be realized by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in a parallelized ortime-divided manner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the shown order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is:
 1. A three-dimensional data encoding method ofencoding three-dimensional points, the three-dimensional data encodingmethod comprising: calculating a predicted value of attributeinformation of a first three-dimensional point in a prediction mode forcalculating the predicted value, using one or more items of attributeinformation of one or more second three-dimensional points in a vicinityof the first three-dimensional point, the first three-dimensional pointand the one or more second three-dimensional points being included inthe three-dimensional points; calculating a prediction residual that isa difference between the attribute information of the firstthree-dimensional point and the predicted value calculated; andgenerating a bitstream including the prediction residual and predictionmode information indicating the prediction mode, wherein the predictionmode is: one prediction mode among two or more prediction modes when atype of the attribute information of the first three-dimensional pointis first attribute information including elements a total number ofwhich is greater than a predetermined threshold value; and one fixedprediction mode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.
 2. The three-dimensional data encodingmethod according to claim 1, wherein the bitstream indicates whether ornot the prediction mode is the one fixed prediction mode.
 3. Thethree-dimensional data encoding method according to claim 1, wherein thepredetermined threshold value is
 1. 4. The three-dimensional dataencoding method according to claim 1, wherein the bitstream does notinclude the prediction mode information when the one prediction mode isthe one fixed prediction mode.
 5. The three-dimensional data encodingmethod according to claim 1, wherein the predicted value is an averagevalue of the one or more items of attribute information of the one ormore second three-dimensional points, when a value indicated by theprediction mode information is
 0. 6. The three-dimensional data encodingmethod according to claim 1, wherein the first attribute information iscolor information indicating color of a three-dimensional point, and thesecond attribute information is information indicating a reflectance ofa three-dimensional point.
 7. A three-dimensional data decoding methodof decoding three-dimensional points, the three-dimensional datadecoding method comprising: obtaining a prediction residual andprediction mode information indicating a prediction mode for a firstthree-dimensional point among the three-dimensional points, by obtaininga bitstream; calculating a predicted value in the prediction modeindicated by the prediction mode information; and calculating attributeinformation of the first three-dimensional point by adding the predictedvalue to the prediction residual, wherein the prediction mode is: oneprediction mode among two or more prediction modes when a type of theattribute information of the first three-dimensional point is firstattribute information including elements a total number of which isgreater than a predetermined threshold value; and one fixed predictionmode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.
 8. The three-dimensional data decodingmethod according to claim 7, wherein the bitstream indicates whether ornot the one prediction mode is the one fixed prediction mode, and thecalculating of the predicted value is performed: in the prediction modeindicated in the prediction mode information when the bitstreamindicates that the one prediction mode is different from the one fixedprediction mode; and in the one fixed prediction mode when the bitstreamindicates that the one prediction mode is the one fixed prediction mode.9. The three-dimensional data decoding method according to claim 7,wherein the predetermined threshold value is
 1. 10. Thethree-dimensional data decoding method according to claim 7, wherein thecalculating of the predicted value is performed in the one fixedprediction mode when the bitstream does not include the prediction modeinformation.
 11. The three-dimensional data decoding method according toclaim 7, wherein the predicted value is an average value of one or moreitems of attribute information of one or more second three-dimensionalpoints in a vicinity of the first three-dimensional point, when a valueindicated by the prediction mode information is
 0. 12. Thethree-dimensional data decoding method according to claim 7, wherein thefirst attribute information is color information indicating color of athree-dimensional point, and the second attribute information isinformation indicating a reflectance of a three-dimensional point.
 13. Athree-dimensional data encoding device that encodes three-dimensionalpoints, the three-dimensional data encoding device comprising: aprocessor; and memory, wherein using the memory, the processor:calculates a predicted value of attribute information of a firstthree-dimensional point in a prediction mode for calculating thepredicted value, using one or more items of attribute information of oneor more second three-dimensional points in a vicinity of the firstthree-dimensional point, the first three-dimensional point and the oneor more second three-dimensional points being included in thethree-dimensional points; calculates a prediction residual that is adifference between the attribute information of the firstthree-dimensional point and the predicted value calculated; andgenerates a bitstream including the prediction residual and predictionmode information indicating the prediction mode, wherein the predictionmode is: one prediction mode among two or more prediction modes when atype of the attribute information of the first three-dimensional pointis first attribute information including elements a total number ofwhich is greater than a predetermined threshold value; and one fixedprediction mode when the type of the attribute information of the firstthree-dimensional point is second attribute information includingelements a total number of which is smaller than or equal to thepredetermined threshold value.
 14. A three-dimensional data decodingdevice that decodes three-dimensional points, the three-dimensional datadecoding device comprising: a processor; and memory, wherein using thememory, the processor: obtains a prediction residual and prediction modeinformation indicating a prediction mode for a first three-dimensionalpoint among the three-dimensional points, by obtaining a bitstream;calculates a predicted value in the prediction mode indicated by theprediction mode information; and calculates attribute information of thefirst three-dimensional point by adding the predicted value to theprediction residual, wherein the prediction mode is: one prediction modeamong two or more prediction modes when a type of the attributeinformation of the first three-dimensional point is first attributeinformation including elements a total number of which is greater than apredetermined threshold value; and one fixed prediction mode when thetype of the attribute information of the first three-dimensional pointis second attribute information including elements a total number ofwhich is smaller than or equal to the predetermined threshold value.